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Massimiliano Marcellino

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

RePEc Biblio mentions

As found on the RePEc Biblio, the curated bibliography of Economics:
  1. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Realtime nowcasting with a Bayesian mixed frequency model with stochastic volatility," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(4), pages 837-862, October.

    Mentioned in:

    1. > Econometrics > Forecasting > Nowcasting
    2. > Econometrics > Time Series Models > VAR Models > Time Varying Parameters and Stochastic Volatility
  2. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Bayesian VARs: Specification Choices and Forecast Accuracy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 46-73, January.

    Mentioned in:

    1. > Econometrics > Time Series Models > VAR Models > Bayesian Vector autoregressions (BVARs)
  3. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.

    Mentioned in:

    1. > Econometrics > Forecasting
  4. Marcellino, Massimiliano & Sivec, Vasja, 2016. "Monetary, fiscal and oil shocks: Evidence based on mixed frequency structural FAVARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 335-348.

    Mentioned in:

    1. > Econometrics > Time Series Models > Dynamic Factor Models > Structural Factor Models

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. Òscar Jordà & Massimiliano Marcellino, 2010. "Path forecast evaluation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 635-662.

    Mentioned in:

    1. Path forecast evaluation (Journal of Applied Econometrics 2010) in ReplicationWiki ()
  2. Massimiliano Marcellino & Carlo A. Favero & Francesca Neglia, 2005. "Principal components at work: the empirical analysis of monetary policy with large data sets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(5), pages 603-620.

    Mentioned in:

    1. Principal components at work: the empirical analysis of monetary policy with large data sets (Journal of Applied Econometrics 2005) in ReplicationWiki ()
  3. Massimiliano Marcellino & Grayham E. Mizon, 2001. "Small-system modelling of real wages, inflation, unemployment and output per capita in Italy 1970-1994," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 359-370.

    Mentioned in:

    1. Small-system modelling of real wages, inflation, unemployment and output per capita in Italy 1970-1994 (Journal of Applied Econometrics 2001) in ReplicationWiki ()
  4. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2011. "Forecasting large datasets with Bayesian reduced rank multivariate models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 735-761, August.

    Mentioned in:

    1. Forecasting large datasets with Bayesian reduced rank multivariate models (Journal of Applied Econometrics 2011) in ReplicationWiki ()

Working papers

  1. Marcellino, Massimiliano & Clark, Todd & Huber, Florian & Koop, Gary & Pfarrhofer, Michael, 2022. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," CEPR Discussion Papers 17461, C.E.P.R. Discussion Papers.

    Cited by:

    1. Florian Huber & Josef Schreiner, 2023. "Are Phillips curves in CESEE still alive and well behaved?," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q3/23, pages 7-27.
    2. Tony Chernis & Niko Hauzenberger & Florian Huber & Gary Koop & James Mitchell, 2023. "Predictive Density Combination Using a Tree-Based Synthesis Function," Working Papers 23-30, Federal Reserve Bank of Cleveland.
    3. Oyebayo Ridwan Olaniran & Ali Rashash R. Alzahrani, 2023. "On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression," Mathematics, MDPI, vol. 11(24), pages 1-29, December.
    4. Jan Pruser & Florian Huber, 2023. "Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions," Papers 2301.13604, arXiv.org, revised Sep 2023.
    5. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Papers 2311.16333, arXiv.org, revised Apr 2024.
    6. Zhemkov, Michael, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, Elsevier, vol. 168(C), pages 10-24.
    7. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    8. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Forecasting euro area inflation with machine-learning models," Research Bulletin, European Central Bank, vol. 112.
    9. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Working Papers 23-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2023.

  2. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino, 2022. "Forecasting US Inflation Using Bayesian Nonparametric Models," Papers 2202.13793, arXiv.org.

    Cited by:

    1. Petar Soric & Enric Monte & Salvador Torra & Oscar Claveria, 2022. ""Density forecasts of inflation using Gaussian process regression models"," IREA Working Papers 202210, University of Barcelona, Research Institute of Applied Economics, revised Jul 2022.
    2. Martin Gachter & Elias Hasler & Florian Huber, 2023. "A tale of two tails: 130 years of growth-at-risk," Papers 2302.08920, arXiv.org.
    3. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Density forecasts of inflation: a quantile regression forest approach," Working Paper Series 2830, European Central Bank.
    4. Jan Pruser & Florian Huber, 2023. "Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions," Papers 2301.13604, arXiv.org, revised Sep 2023.
    5. Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022. "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers 2212.03471, arXiv.org, revised Jul 2023.
    6. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.

  3. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2022. "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," CEPR Discussion Papers 17512, C.E.P.R. Discussion Papers.

    Cited by:

    1. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    2. Paul Labonne & Leif Anders Thorsrud, 2023. "Risky news and credit market sentiment," Working Papers No 14/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    3. Gefang, Deborah & Koop, Gary & Poon, Aubrey, 2023. "Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage," International Journal of Forecasting, Elsevier, vol. 39(1), pages 346-363.
    4. Marcellino, Massimiliano & Clark, Todd & Carriero, Andrea, 2021. "Nowcasting Tail Risk to Economic Activity at a Weekly Frequency," CEPR Discussion Papers 16496, C.E.P.R. Discussion Papers.
    5. Martin Gachter & Elias Hasler & Florian Huber, 2023. "A tale of two tails: 130 years of growth-at-risk," Papers 2302.08920, arXiv.org.
    6. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    7. Patrick A. Adams & Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2020. "Forecasting Macroeconomic Risks," Staff Reports 914, Federal Reserve Bank of New York.
    8. Kiss, Tamás & Nguyen, Hoang & Österholm, Pär, 2022. "The Relation between the High-Yield Bond Spread and the Unemployment Rate in the Euro Area," Finance Research Letters, Elsevier, vol. 46(PA).
    9. Leopoldo Catania & Alessandra Luati & Pierluigi Vallarino, 2021. "Economic vulnerability is state dependent," CREATES Research Papers 2021-09, Department of Economics and Business Economics, Aarhus University.
    10. Sune Karlsson & Stepan Mazur & Hoang Nguyen, 2021. "Vector autoregression models with skewness and heavy tails," Papers 2105.11182, arXiv.org.
    11. Tamás Kiss & Stepan Mazur & Hoang Nguyen & Pär Österholm, 2023. "Modeling the relation between the US real economy and the corporate bond‐yield spread in Bayesian VARs with non‐Gaussian innovations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 347-368, March.
    12. Szendrei, Tibor & Varga, Katalin, 2023. "Revisiting vulnerable growth in the Euro Area: Identifying the role of financial conditions in the distribution," Economics Letters, Elsevier, vol. 223(C).
    13. Falconio, Andrea & Manganelli, Simone, 2020. "Financial conditions, business cycle fluctuations and growth at risk," Working Paper Series 2470, European Central Bank.
    14. Anthony Garratt & Timo Henckel & Shaun P. Vahey, 2019. "Empirically-transformed linear opinion pools," CAMA Working Papers 2019-47, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    15. Deng, Chuang & Wu, Jian, 2023. "Macroeconomic downside risk and the effect of monetary policy," Finance Research Letters, Elsevier, vol. 54(C).
    16. Mihail Yanchev, 2022. "Deep Growth-at-Risk Model: Nowcasting the 2020 Pandemic Lockdown Recession in Small Open Economies," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 7, pages 20-41.

  4. Marcellino, Massimiliano & Bai, Yu & Carriero, Andrea & Clark, Todd, 2022. "Macroeconomic Forecasting in a Multi-country Context," CEPR Discussion Papers 16994, C.E.P.R. Discussion Papers.

    Cited by:

    1. Niko Hauzenberger & Florian Huber & Karin Klieber & Massimiliano Marcellino, 2022. "Bayesian Neural Networks for Macroeconomic Analysis," Papers 2211.04752, arXiv.org, revised Apr 2024.

  5. Foroni, Claudia & Gelain, Paolo & Marcellino, Massimiliano, 2022. "The financial accelerator mechanism: does frequency matter?," Working Paper Series 2637, European Central Bank.

    Cited by:

    1. Gelfer, Sacha & Gibbs, Christopher G., 2023. "Measuring the effects of large-scale asset purchases: The role of international financial markets and the financial accelerator," Journal of International Money and Finance, Elsevier, vol. 131(C).
    2. Guido Ascari & Qazi Haque & Leandro M. Magnusson & Sophocles Mavroeidis, 2021. "Empirical evidence on the Euler equation for investment in the US," CAMA Working Papers 2021-65, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

  6. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Specification Choices in Quantile Regression for Empirical Macroeconomics," Working Papers 22-25, Federal Reserve Bank of Cleveland.

    Cited by:

    1. Jan Pruser & Florian Huber, 2023. "Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions," Papers 2301.13604, arXiv.org, revised Sep 2023.

  7. Andrea Carriero & Massimiliano Marcellino & Tommaso Tornese, 2022. "Macro Uncertainty in the Long Run," BAFFI CAREFIN Working Papers 22188, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.

    Cited by:

    1. Mathias Krogh & Giovanni Pellegrino, "undated". "Real Activity and Uncertainty Shocks: The Long and the Short of It," "Marco Fanno" Working Papers 0310, Dipartimento di Scienze Economiche "Marco Fanno".

  8. Marcellino, Massimiliano & Clark, Todd & Carriero, Andrea, 2021. "Nowcasting Tail Risk to Economic Activity at a Weekly Frequency," CEPR Discussion Papers 16496, C.E.P.R. Discussion Papers.

    Cited by:

    1. Matteo Iacopini & Francesco Ravazzolo & Luca Rossini, 2022. "Bayesian Multivariate Quantile Regression with alternative Time-varying Volatility Specifications," Papers 2211.16121, arXiv.org.
    2. Narasingha Das & Partha Gangopadhyay, 2023. "Did weekly economic index and volatility index impact US food sales during the first year of the pandemic?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-23, December.
    3. Eraslan, Sercan & Reif, Magnus, 2023. "A latent weekly GDP indicator for Germany," Technical Papers 08/2023, Deutsche Bundesbank.
    4. Paul Labonne, 2022. "Asymmetric Uncertainty: Nowcasting Using Skewness in Real-time Data," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-23, Economic Statistics Centre of Excellence (ESCoE).
    5. Matteo Iacopini & Aubrey Poon & Luca Rossini & Dan Zhu, 2024. "A Quantile Nelson-Siegel model," Papers 2401.09874, arXiv.org.

  9. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino & Elmar Mertens, 2021. "Forecasting with Shadow-Rate VARs," Working Papers 21-09, Federal Reserve Bank of Cleveland.

    Cited by:

    1. Schorfheide, Frank & Aruoba, Boragan & Mlikota, Marko & Villalvazo, Sergio, 2021. "SVARs With Occasionally-Binding Constraints," CEPR Discussion Papers 15923, C.E.P.R. Discussion Papers.

  10. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2021. "Using Time-Varying Volatility for Identification in Vector Autoregressions: An Application to Endogenous Uncertainty," CEPR Discussion Papers 16346, C.E.P.R. Discussion Papers.

    Cited by:

    1. Olli Palm'en, 2022. "Macroeconomic Effect of Uncertainty and Financial Shocks: a non-Gaussian VAR approach," Papers 2202.10834, arXiv.org.
    2. Gnangnon, Sèna Kimm, 2023. "Effect of Economic Uncertainty on Remittances Flows from Developed Countries," EconStor Preprints 279480, ZBW - Leibniz Information Centre for Economics.
    3. Andrea Carriero & Alessio Volpicella, 2022. "Generalizing the Max Share Identification to multiple shocks identification: an Application to Uncertainty," School of Economics Discussion Papers 0322, School of Economics, University of Surrey.
    4. Andrea Carriero & Massimiliano Marcellino & Tommaso Tornese, 2022. "Macro Uncertainty in the Long Run," BAFFI CAREFIN Working Papers 22188, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    5. Sune Karlsson & Stepan Mazur & Hoang Nguyen, 2021. "Vector autoregression models with skewness and heavy tails," Papers 2105.11182, arXiv.org.
    6. Beckmann, Joscha & Czudaj, Robert L., 2024. "Uncertainty Shocks and Inflation: The Role of Credibility and Expectation Anchoring," MPRA Paper 119971, University Library of Munich, Germany.
    7. Dibiasi, Andreas & Sarferaz, Samad, 2023. "Measuring macroeconomic uncertainty: A cross-country analysis," European Economic Review, Elsevier, vol. 153(C).
    8. Ambrocio, Gene, 2020. "Inflationary household uncertainty shocks," Bank of Finland Research Discussion Papers 5/2020, Bank of Finland.

  11. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd & Mertens, Elmar, 2021. "Measuring Uncertainty and Its Effects in the COVID-19 Era," CEPR Discussion Papers 15965, C.E.P.R. Discussion Papers.

    Cited by:

    1. Cross, Jamie L. & Hou, Chenghan & Koop, Gary & Poon, Aubrey, 2023. "Large stochastic volatility in mean VARs," Journal of Econometrics, Elsevier, vol. 236(1).
    2. Luis J. Álvarez & Florens Odendahl, 2022. "Data outliers and Bayesian VARs in the Euro Area," Working Papers 2239, Banco de España.

  12. Massimo Guidolin & Davide La Cara & Massimiliano Marcellino, 2021. "Boosting the Forecasting Power of Conditional Heteroskedasticity Models to Account for Covid-19 Outbreaks," BAFFI CAREFIN Working Papers 21169, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.

    Cited by:

    1. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Papers 2311.16333, arXiv.org, revised Apr 2024.
    2. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Working Papers 23-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2023.
    3. Beatrice Franzolini & Alexandros Beskos & Maria De Iorio & Warrick Poklewski Koziell & Karolina Grzeszkiewicz, 2022. "Change point detection in dynamic Gaussian graphical models: the impact of COVID-19 pandemic on the US stock market," Papers 2208.00952, arXiv.org, revised May 2023.

  13. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2021. "Investigating Growth at Risk Using a Multi-country Non-parametric Quantile Factor Model," Papers 2110.03411, arXiv.org.

    Cited by:

    1. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    2. Dimitris Korobilis & Maximilian Schroder, 2023. "Monitoring multicountry macroeconomic risk," Papers 2305.09563, arXiv.org.
    3. Dimitris Korobilis & Maximilian Schröder, 2023. "Probabilistic Quantile Factor Analysis," Working Papers No 05/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.

  14. Marcellino, Massimiliano & Clark, Todd & Carriero, Andrea & Mertens, Elmar, 2021. "Addressing COVID-19 Outliers in BVARs with Stochastic Volatility," CEPR Discussion Papers 15964, C.E.P.R. Discussion Papers.

    Cited by:

    1. Cross, Jamie L. & Hou, Chenghan & Koop, Gary & Poon, Aubrey, 2023. "Large stochastic volatility in mean VARs," Journal of Econometrics, Elsevier, vol. 236(1).
    2. Knüppel, Malte & Krüger, Fabian & Pohle, Marc-Oliver, 2022. "Score-based calibration testing for multivariate forecast distributions," Discussion Papers 50/2022, Deutsche Bundesbank.
    3. Daniele Valenti & Andrea Bastianin & Matteo Manera, 2022. "A weekly structural VAR model of the US crude oil market," Working Papers 2022.11, Fondazione Eni Enrico Mattei.
    4. David Kohns & Arnab Bhattacharjee, 2020. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," Papers 2011.00938, arXiv.org, revised May 2022.
    5. Davidson, Sharada Nia & Moccero, Diego Nicolas, 2024. "The nonlinear effects of banks’ vulnerability to capital depletion in euro area countries," Working Paper Series 2912, European Central Bank.
    6. Evgenidis, Anastasios & Fasianos, Apostolos, 2023. "Modelling monetary policy’s impact on labour markets under Covid-19," Economics Letters, Elsevier, vol. 230(C).
    7. Joshua C. C. Chan, 2022. "Comparing Stochastic Volatility Specifications for Large Bayesian VARs," Papers 2208.13255, arXiv.org.
    8. Hwee Kwan Chow & Keen Meng Choy, 2023. "Economic forecasting in a pandemic: some evidence from Singapore," Empirical Economics, Springer, vol. 64(5), pages 2105-2124, May.
    9. Frank Schorfheide & Dongho Song, 2020. "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," PIER Working Paper Archive 20-039, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    10. Prüser, Jan, 2021. "The horseshoe prior for time-varying parameter VARs and Monetary Policy," Journal of Economic Dynamics and Control, Elsevier, vol. 129(C).
    11. Bobeica, Elena & Hartwig, Benny, 2023. "The COVID-19 shock and challenges for inflation modelling," International Journal of Forecasting, Elsevier, vol. 39(1), pages 519-539.
    12. Sune Karlsson & Stepan Mazur & Hoang Nguyen, 2021. "Vector autoregression models with skewness and heavy tails," Papers 2105.11182, arXiv.org.
    13. Budnik, Katarzyna & Groß, Johannes & Vagliano, Gianluca & Dimitrov, Ivan & Lampe, Max & Panos, Jiri & Velasco, Sofia & Boucherie, Louis & Jančoková, Martina, 2023. "BEAST: A model for the assessment of system-wide risks and macroprudential policies," Working Paper Series 2855, European Central Bank.
    14. Luis J. Álvarez & Florens Odendahl, 2022. "Data outliers and Bayesian VARs in the Euro Area," Working Papers 2239, Banco de España.
    15. Kiss, Tamas & Nguyen, Hoang & Österholm, Pär, 2022. "Modelling Okun’s Law – Does non-Gaussianity Matter?," Working Papers 2022:1, Örebro University, School of Business.
    16. Serena Ng, 2021. "Modeling Macroeconomic Variations after Covid-19," NBER Working Papers 29060, National Bureau of Economic Research, Inc.
    17. Marcellino, Massimiliano & Clark, Todd & Huber, Florian & Koop, Gary & Pfarrhofer, Michael, 2022. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," CEPR Discussion Papers 17461, C.E.P.R. Discussion Papers.
    18. Bańbura, Marta & Belousova, Irina & Bodnár, Katalin & Tóth, Máté Barnabás, 2023. "Nowcasting employment in the euro area," Working Paper Series 2815, European Central Bank.
    19. Danilo Cascaldi-Garcia, 2022. "Pandemic Priors," International Finance Discussion Papers 1352, Board of Governors of the Federal Reserve System (U.S.).
    20. Morley, James & Palenzuela, Diego Rodriguez & Sun, Yiqiao & Wong, Benjamin, 2022. "Estimating the Euro Area output gap using multivariate information and addressing the COVID-19 pandemic," Working Paper Series 2716, European Central Bank.
    21. Barend Abeln & Jan P.A.M. Jacobs, 2021. "COVID19 and Seasonal Adjustment," CIRANO Working Papers 2021s-05, CIRANO.
    22. Alanya-Beltran, Willy, 2022. "Unit roots in lower-bounded series with outliers," Economic Modelling, Elsevier, vol. 115(C).
    23. Saeed Zaman, 2021. "A Unified Framework to Estimate Macroeconomic Stars," Working Papers 21-23R, Federal Reserve Bank of Cleveland, revised 15 Aug 2022.
    24. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Working Papers 22-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    25. Nalban, Valeriu & Smădu, Andra, 2021. "Asymmetric effects of uncertainty shocks: Normal times and financial disruptions are different," Journal of Macroeconomics, Elsevier, vol. 69(C).
    26. Florian Huber, 2023. "Bayesian Nonlinear Regression using Sums of Simple Functions," Papers 2312.01881, arXiv.org.
    27. Vito Polito & Yunyi Zhang, 2021. "Tackling Large Outliers in Macroeconomic Data with Vector Artificial Neural Network Autoregression," CESifo Working Paper Series 9395, CESifo.
    28. Chan, Joshua C.C. & Poon, Aubrey & Zhu, Dan, 2023. "High-dimensional conditionally Gaussian state space models with missing data," Journal of Econometrics, Elsevier, vol. 236(1).
    29. Barauskaitė, Kristina & Nguyen, Anh D.M. & Rousová, Linda & Cappiello, Lorenzo, 2022. "The impact of credit supply shocks in the euro area: market-based financing versus loans," Working Paper Series 2673, European Central Bank.
    30. Kohns, David & Bhattacharjee, Arnab, 2023. "Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1384-1412.
    31. Sun, Weihong & Liu, Ding, 2023. "Great moderation with Chinese characteristics: Uncovering the role of monetary policy," Economic Modelling, Elsevier, vol. 121(C).
    32. Eraslan, Sercan & Schröder, Maximilian, 2023. "Nowcasting GDP with a pool of factor models and a fast estimation algorithm," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1460-1476.
    33. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino & Elmar Mertens, 2021. "Forecasting with Shadow-Rate VARs," Working Papers 21-09, Federal Reserve Bank of Cleveland.
    34. Colunga L. Fernando & Torre Cepeda Leonardo, 2023. "Effects of Supply, Demand, and Labor Market Shocks in the Mexican Manufacturing Sector," Working Papers 2023-10, Banco de México.
    35. Mertens, Elmar, 2023. "Precision-based sampling for state space models that have no measurement error," Journal of Economic Dynamics and Control, Elsevier, vol. 154(C).
    36. Bańbura, Marta & Bobeica, Elena & Martínez Hernández, Catalina, 2023. "What drives core inflation? The role of supply shocks," Working Paper Series 2875, European Central Bank.
    37. Andrejs Zlobins, 2021. "On the Time-varying Effects of the ECB's Asset Purchases," Working Papers 2021/02, Latvijas Banka.
    38. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org.

  15. Niko Hauzenberger & Florian Huber & Massimiliano Marcellino & Nico Petz, 2021. "Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty," Papers 2112.01995, arXiv.org, revised Nov 2022.

    Cited by:

    1. Andrea Renzetti, 2023. "Theory coherent shrinkage of Time-Varying Parameters in VARs," Papers 2311.11858, arXiv.org.

  16. Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2021. "Can Machine Learning Catch the COVID-19 Recession?," Papers 2103.01201, arXiv.org.

    Cited by:

    1. Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022. "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    2. Philippe Goulet Coulombe, 2021. "Slow-Growing Trees," Papers 2103.01926, arXiv.org, revised Jul 2021.
    3. Goulet Coulombe, Philippe & Leroux, Maxime & Stevanovic, Dalibor & Surprenant, Stéphane, 2021. "Macroeconomic data transformations matter," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1338-1354.
    4. Marcellino, Massimiliano & Clark, Todd & Huber, Florian & Koop, Gary, 2023. "Forecasting US Inflation Using Bayesian Nonparametric Models," CEPR Discussion Papers 18244, C.E.P.R. Discussion Papers.
    5. Paul Ho, 2021. "Forecasting in the Absence of Precedent," Working Paper 21-10, Federal Reserve Bank of Richmond.
    6. Philippe Goulet Coulombe, 2021. "Slow-Growing Trees," Working Papers 21-02, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    7. Marcellino, Massimiliano & Clark, Todd & Huber, Florian & Koop, Gary & Pfarrhofer, Michael, 2022. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," CEPR Discussion Papers 17461, C.E.P.R. Discussion Papers.
    8. Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
    9. Philippe Goulet Coulombe, 2021. "To Bag is to Prune," Working Papers 21-03, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Jun 2021.
    10. Hauzenberger, Niko & Huber, Florian & Klieber, Karin, 2023. "Real-time inflation forecasting using non-linear dimension reduction techniques," International Journal of Forecasting, Elsevier, vol. 39(2), pages 901-921.
    11. James T. E. Chapman & Ajit Desai, 2022. "Macroeconomic Predictions using Payments Data and Machine Learning," Papers 2209.00948, arXiv.org.
    12. Byron Botha & Rulof Burger & Kevin Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," School of Economics Macroeconomic Discussion Paper Series 2022-03, School of Economics, University of Cape Town.
    13. Zhemkov, Michael, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, Elsevier, vol. 168(C), pages 10-24.

  17. Marcellino, Massimiliano & Kapetanios, George & Dendramis, Yiannis, 2020. "A Similarity-based Approach for Macroeconomic Forecasting," CEPR Discussion Papers 14469, C.E.P.R. Discussion Papers.

    Cited by:

    1. Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers 15114, C.E.P.R. Discussion Papers.
    2. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    3. Dong Hwan Oh & Andrew J. Patton, 2021. "Better the Devil You Know: Improved Forecasts from Imperfect Models," Finance and Economics Discussion Series 2021-071, Board of Governors of the Federal Reserve System (U.S.).

  18. Marcellino, Massimiliano & Kapetanios, George & Giraitis, Liudas, 2020. "Time-Varying Instrumental Variable Estimation," CEPR Discussion Papers 15210, C.E.P.R. Discussion Papers.

    Cited by:

    1. Michele Fratianni & Federico Giri & Riccardo Lucchetti & Francesco Valentini, 2022. "Monetization, wars, and the Italian fiscal multiplier," Mo.Fi.R. Working Papers 176, Money and Finance Research group (Mo.Fi.R.) - Univ. Politecnica Marche - Dept. Economic and Social Sciences.
    2. Lucchetti, Riccardo & Valentini, Francesco, 2021. "Kernel-based Time-Varying IV estimation: handle with care," MPRA Paper 110033, University Library of Munich, Germany.
    3. Yu Bai & Massimiliano Marcellino & George Kapetanios, 2023. "Mean Group Instrumental Variable Estimation of Time-Varying Large Heterogeneous Panels with Endogenous Regressors," Monash Econometrics and Business Statistics Working Papers 13/23, Monash University, Department of Econometrics and Business Statistics.
    4. Fu, Zhonghao & Hong, Yongmiao & Su, Liangjun & Wang, Xia, 2023. "Specification tests for time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 235(2), pages 720-744.

  19. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.

    Cited by:

    1. Aaron J. Amburgey & Michael W. McCracken, 2023. "On the real‐time predictive content of financial condition indices for growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 137-163, March.
    2. Hager Ben Romdhane, 2021. "Nowcasting in Tunisia using large datasets and mixed frequency models," IHEID Working Papers 11-2021, Economics Section, The Graduate Institute of International Studies.
    3. David Kohns & Tibor Szendrei, 2021. "Decoupling Shrinkage and Selection for the Bayesian Quantile Regression," Papers 2107.08498, arXiv.org.
    4. Matteo Iacopini & Francesco Ravazzolo & Luca Rossini, 2020. "Proper scoring rules for evaluating asymmetry in density forecasting," Working Papers No 06/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    5. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    6. Laurent Ferrara & Matteo Mogliani & Jean-Guillaume Sahuc, 2020. "High-frequency monitoring of growth-at-risk," CAMA Working Papers 2020-97, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    7. Ana Beatriz Galvão & Michael Owyang, 2022. "Forecasting low‐frequency macroeconomic events with high‐frequency data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1314-1333, November.
    8. Frank Schorfheide & Dongho Song, 2020. "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," PIER Working Paper Archive 20-039, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    9. Lenza, Michele & Cimadomo, Jacopo & Giannone, Domenico & Monti, Francesca & Sokol, Andrej, 2021. "Nowcasting with Large Bayesian Vector Autoregressions," CEPR Discussion Papers 15854, C.E.P.R. Discussion Papers.
    10. Gloria Gonzalez-Rivera & Vladimir Rodriguez-Caballero & Esther Ruiz, 2021. "Expecting the unexpected: economic growth under stress," Working Papers 202106, University of California at Riverside, Department of Economics.
    11. Teng, Bin & Wang, Sicong & Shi, Yufeng & Sun, Yunchuan & Wang, Wei & Hu, Wentao & Shi, Chaojun, 2022. "Economic recovery forecasts under impacts of COVID-19," Economic Modelling, Elsevier, vol. 110(C).
    12. Korobilis, Dimitris & Landau, Bettina & Musso, Alberto & Phella, Anthoulla, 2021. "The time-varying evolution of inflation risks," Working Paper Series 2600, European Central Bank.
    13. Alina Stundziene & Vaida Pilinkiene & Jurgita Bruneckiene & Andrius Grybauskas & Mantas Lukauskas, 2023. "Nowcasting Economic Activity Using Electricity Market Data: The Case of Lithuania," Economies, MDPI, vol. 11(5), pages 1-21, May.
    14. Jennifer Betz & Maximilian Nagl & Daniel Rösch, 2022. "Credit line exposure at default modelling using Bayesian mixed effect quantile regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2035-2072, October.
    15. Tino Berger & James Morley & Benjamin Wong, 2020. "Nowcasting the output gap," CAMA Working Papers 2020-78, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    16. Yfanti, Stavroula & Karanasos, Menelaos & Zopounidis, Constantin & Christopoulos, Apostolos, 2023. "Corporate credit risk counter-cyclical interdependence: A systematic analysis of cross-border and cross-sector correlation dynamics," European Journal of Operational Research, Elsevier, vol. 304(2), pages 813-831.
    17. Stolbov, Mikhail & Shchepeleva, Maria, 2022. "Modeling global real economic activity: Evidence from variable selection across quantiles," The Journal of Economic Asymmetries, Elsevier, vol. 25(C).
    18. Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers 15114, C.E.P.R. Discussion Papers.
    19. Salisu, Afees A. & Ogbonna, Ahamuefula E. & Vo, Xuan Vinh, 2023. "Oil tail risks and the realized variance of consumer prices in advanced economies," Resources Policy, Elsevier, vol. 83(C).
    20. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.
    21. Wegmüller, Philipp & Glocker, Christian & Guggia, Valentino, 2023. "Weekly economic activity: Measurement and informational content," International Journal of Forecasting, Elsevier, vol. 39(1), pages 228-243.
    22. Hans Genberg & Özer Karagedikli, 2021. "Machine Learning and Central Banks: Ready for Prime Time?," Working Papers wp43, South East Asian Central Banks (SEACEN) Research and Training Centre.

  20. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2020. "The economic drivers of volatility and uncertainty," Temi di discussione (Economic working papers) 1285, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Marcellino, Massimiliano & Carriero, Andrea & Corsello, Francesco, 2019. "The Global Component of Inflation Volatility," CEPR Discussion Papers 13470, C.E.P.R. Discussion Papers.
    2. Gianluca Cubadda & Stefano Grassi & Barbara Guardabascio, 2024. "The Time-Varying Multivariate Autoregressive Index Model," CEIS Research Paper 571, Tor Vergata University, CEIS, revised 10 Jan 2024.

  21. Claudia Foroni & Massimiliano Marcellino & Dalibor Stevanovic, 2020. "Forecasting the COVID-19 recession and recovery: Lessons from the financial crisis," Working Papers 20-14, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2020.

    Cited by:

    1. Sebastian Doerr & Leonardo Gambacorta & José María Serena Garralda, 2021. "Big data and machine learning in central banking," BIS Working Papers 930, Bank for International Settlements.
    2. Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022. "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    3. Christiane Baumeister & Pierre Guérin, 2020. "A Comparison of Monthly Global Indicators for Forecasting Growth," CESifo Working Paper Series 8656, CESifo.
    4. Yose Rizal Damuri & Prabaning Tyas & Haryo Aswicahyono & Lionel Priyadi & Stella Kusumawardhani & Ega Kurnia Yazid, 2021. "Tracking the Ups and Downs in Indonesia’s Economic Activity During COVID-19 Using Mobility Index: Evidence from Provinces in Java and Bali," Working Papers DP-2021-18, Economic Research Institute for ASEAN and East Asia (ERIA).
    5. Frank Schorfheide & Dongho Song, 2020. "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," PIER Working Paper Archive 20-039, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    6. Kevin Moran & Dalibor Stevanovic & Adam Kader Touré, 2022. "Macroeconomic uncertainty and the COVID‐19 pandemic: Measure and impacts on the Canadian economy," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(S1), pages 379-405, February.
    7. De Backer, Bruno & Dewachter, Hans & Iania, Leonardo, 2021. "Macrofinancial information on the post- COVID-19 economic recovery: will it be V, U or L-shaped?," LIDAM Discussion Papers LFIN 2021002, Université catholique de Louvain, Louvain Finance (LFIN).
    8. Shafiullah Qureshi & Ba Chu & Fanny S. Demers, 2021. "Forecasting Canadian GDP Growth with Machine Learning," Carleton Economic Papers 21-05, Carleton University, Department of Economics.
    9. Cassetti, Gabriele & Boitier, Baptiste & Elia, Alessia & Le Mouël, Pierre & Gargiulo, Maurizio & Zagamé, Paul & Nikas, Alexandros & Koasidis, Konstantinos & Doukas, Haris & Chiodi, Alessandro, 2023. "The interplay among COVID-19 economic recovery, behavioural changes, and the European Green Deal: An energy-economic modelling perspective," Energy, Elsevier, vol. 263(PC).
    10. Zhao, Xinyue & Chen, Heng & Zheng, Qiwei & Liu, Jun & Pan, Peiyuan & Xu, Gang & Zhao, Qinxin & Jiang, Xue, 2023. "Thermo-economic analysis of a novel hydrogen production system using medical waste and biogas with zero carbon emission," Energy, Elsevier, vol. 265(C).
    11. Teng, Bin & Wang, Sicong & Shi, Yufeng & Sun, Yunchuan & Wang, Wei & Hu, Wentao & Shi, Chaojun, 2022. "Economic recovery forecasts under impacts of COVID-19," Economic Modelling, Elsevier, vol. 110(C).
    12. Paul Ho, 2021. "Forecasting in the Absence of Precedent," Working Paper 21-10, Federal Reserve Bank of Richmond.
    13. Rybacki, Jakub & Gniazdowski, Michał, 2021. "Macroeconomic Forecasting in Poland: Lessons From the COVID-19 Outbreak," MPRA Paper 107682, University Library of Munich, Germany.
    14. Serena Ng, 2021. "Modeling Macroeconomic Variations after Covid-19," NBER Working Papers 29060, National Bureau of Economic Research, Inc.
    15. Daniel Hopp, 2022. "Performance of long short-term memory artificial neural networks in nowcasting during the COVID-19 crisis," Papers 2203.11872, arXiv.org.
    16. Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
    17. İsmail Cakmak & Selcen Öztürk, 2023. "Analysing Impact of Economic Crises on Sector Profits with a New Approach," Prague Economic Papers, Prague University of Economics and Business, vol. 2023(3), pages 225-245.
    18. James T. E. Chapman & Ajit Desai, 2022. "Macroeconomic Predictions using Payments Data and Machine Learning," Papers 2209.00948, arXiv.org.
    19. Nugroho, Anggoro Dimas Pambudi, 2022. "Strategi Ekonomi Bisnis dalam Upaya Menghadapi Ancaman Resesi 2023," OSF Preprints j3dpm, Center for Open Science.
    20. Valentina Aprigliano & Alessandro Borin & Francesco Paolo Conteduca & Simone Emiliozzi & Marco Flaccadoro & Sabina Marchetti & Stefania Villa, 2021. "Forecasting Italian GDP growth with epidemiological data," Questioni di Economia e Finanza (Occasional Papers) 664, Bank of Italy, Economic Research and International Relations Area.
    21. Richard B. Freeman, 2022. "Planning for the “Expected Unexpected”: Work and Retirement in the U.S. After the COVID-19 Pandemic Shock," NBER Working Papers 29653, National Bureau of Economic Research, Inc.
    22. Lorenzo Fratoni & Susanna Levantesi & Massimiliano Menzietti, 2022. "Measuring Financial Sustainability and Social Adequacy of the Italian NDC Pension System under the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(23), pages 1-23, December.
    23. Orkideh Gharehgozli & Sunhyung Lee, 2022. "Money Supply and Inflation after COVID-19," Economies, MDPI, vol. 10(5), pages 1-14, April.
    24. Zhemkov, Michael, 2021. "Nowcasting Russian GDP using forecast combination approach," International Economics, Elsevier, vol. 168(C), pages 10-24.
    25. Bas Scheer, 2022. "Addressing Unemployment Rate Forecast Errors in Relation to the Business Cycle," CPB Discussion Paper 434, CPB Netherlands Bureau for Economic Policy Analysis.
    26. John O’Trakoun, 2022. "Business forecasting during the pandemic," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 57(3), pages 95-110, July.
    27. Yannis Psycharis & Anastasia Panori & Dimitrios Athanasopoulos, 2022. "Public Investment and Regional Resilience: Empirical Evidence from the Greek Regions," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 113(1), pages 57-79, February.
    28. Fezzi, Carlo & Fanghella, Valeria, 2021. "Tracking GDP in real-time using electricity market data: Insights from the first wave of COVID-19 across Europe," European Economic Review, Elsevier, vol. 139(C).
    29. Serena Ng & Susannah Scanlan, 2023. "Constructing High Frequency Economic Indicators by Imputation," Papers 2303.01863, arXiv.org, revised Oct 2023.
    30. Nadiia Shapovalenko, 2021. "A BVAR Model for Forecasting Ukrainian Inflation," IHEID Working Papers 05-2021, Economics Section, The Graduate Institute of International Studies.
    31. Suckert, Lisa, 2021. "Von der Pandemie zu einer Neuordnung der Zeit? Zeitsoziologische Perspektiven auf das Verhältnis von Zeitlichkeit, Wirtschaft und Staat," MPIfG Discussion Paper 21/7, Max Planck Institute for the Study of Societies.
    32. Jakub Rybacki & Michał Gniazdowski, 2023. "Macroeconomic forecasting in Poland: lessons from the external shocks," Bank i Kredyt, Narodowy Bank Polski, vol. 54(1), pages 45-64.
    33. Arbolino, Roberta & Caro, Paolo Di, 2021. "Can the EU funds promote regional resilience at time of Covid-19? Insights from the Great Recession11We thank the Editors and the four anonymous referees for helpful comments. We also thank Emanuele C," Journal of Policy Modeling, Elsevier, vol. 43(1), pages 109-126.
    34. Severin Reissl & Alessandro Caiani & Francesco Lamperti & Mattia Guerini & Fabio Vanni & Giorgio Fagiolo & Tommaso Ferraresi & Leonardo Ghezzi & Mauro Napoletano & Andrea Roventini, 2021. "Assessing the economic effects of lockdowns in Italy: a computational Input-Output approach," LEM Papers Series 2021/03, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    35. Archanskaia, Elizaveta & Canton, Erik & Hobza, Alexandr & Nikolov, Plamen & Simons, Wouter, 2023. "The asymmetric impact of COVID-19: A novel approach to quantifying financial distress across industries," European Economic Review, Elsevier, vol. 158(C).
    36. Antonio Oliva & Francesco Gracceva & Daniele Lerede & Matteo Nicoli & Laura Savoldi, 2021. "Projection of Post-Pandemic Italian Industrial Production through Vector AutoRegressive Models," Energies, MDPI, vol. 14(17), pages 1-18, September.
    37. Wang, Yuting & Chen, Heng & Qiao, Shichao & Pan, Peiyuan & Xu, Gang & Dong, Yuehong & Jiang, Xue, 2023. "A novel methanol-electricity cogeneration system based on the integration of water electrolysis and plasma waste gasification," Energy, Elsevier, vol. 267(C).

  22. Marcellino, Massimiliano & Clark, Todd & Carriero, Andrea, 2019. "Assessing International Commonality in Macroeconomic Uncertainty and Its Effects," CEPR Discussion Papers 13970, C.E.P.R. Discussion Papers.

    Cited by:

    1. Jose E. Gomez-Gonzalez & Jorge Hirs-Garzon & Jorge M. Uribe, 2020. "Global effects of US uncertainty: real and financial shocks on real and financial markets," IREA Working Papers 202015, University of Barcelona, Research Institute of Applied Economics, revised Oct 2020.
    2. Cross, Jamie L. & Hou, Chenghan & Koop, Gary & Poon, Aubrey, 2023. "Large stochastic volatility in mean VARs," Journal of Econometrics, Elsevier, vol. 236(1).
    3. Efrem Castelnuovo, 2022. "Uncertainty Before and During COVID-19: A Survey," "Marco Fanno" Working Papers 0279, Dipartimento di Scienze Economiche "Marco Fanno".
    4. Bobasu, Alina & Geis, André & Quaglietti, Lucia & Ricci, Martino, 2021. "Tracking global economic uncertainty: implications for the euro area," Working Paper Series 2541, European Central Bank.
    5. Andrea Carriero & Alessio Volpicella, 2022. "Generalizing the Max Share Identification to multiple shocks identification: an Application to Uncertainty," School of Economics Discussion Papers 0322, School of Economics, University of Surrey.
    6. Lodge, David & Pérez, Javier J. & Albrizio, Silvia & Everett, Mary & De Bandt, Olivier & Georgiadis, Georgios & Ca' Zorzi, Michele & Lastauskas, Povilas & Carluccio, Juan & Parrága, Susana & Carvalho,, 2021. "The implications of globalisation for the ECB monetary policy strategy," Occasional Paper Series 263, European Central Bank.
    7. Arigoni, Filippo & Lenarcic, Crt, 2023. "Foreign economic policy uncertainty shocks and real activity in the Euro area," Research Technical Papers 7/RT/23, Central Bank of Ireland.
    8. Andreas Dibiasi & Samad Sarferaz, 2020. "Measuring Macroeconomic Uncertainty: The Labor Channel of Uncertainty from a Cross-Country Perspective," Papers 2006.09007, arXiv.org, revised Dec 2020.
    9. Jamie L. Cross & Chenghan Hou & Aubrey Poon, 2018. "International Transmission of Macroeconomic Uncertainty in Small Open Economies: An Empirical Approach," Working Papers No 12/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    10. Bonciani, Dario & Ricci, Martino, 2020. "The international effects of global financial uncertainty shocks," Journal of International Money and Finance, Elsevier, vol. 109(C).
    11. Dibiasi, Andreas & Sarferaz, Samad, 2023. "Measuring macroeconomic uncertainty: A cross-country analysis," European Economic Review, Elsevier, vol. 153(C).
    12. Crespo Cuaresma, Jesús & Huber, Florian & Onorante, Luca, 2020. "Fragility and the effect of international uncertainty shocks," Journal of International Money and Finance, Elsevier, vol. 108(C).
    13. Iader Giraldo & Carlos Giraldo & José E. Gomez-Gonzalez & Jorge Mario Uribe, 2023. "US uncertainty shocks, credit, production, and prices: The case of fourteen Latin American countries," Documentos de trabajo 20667, FLAR.
    14. Miescu, Mirela S., 2023. "Uncertainty shocks in emerging economies: A global to local approach for identification," European Economic Review, Elsevier, vol. 154(C).
    15. Nina Biljanovska & Mr. Francesco Grigoli & Martina Hengge, 2017. "Fear Thy Neighbor: Spillovers from Economic Policy Uncertainty," IMF Working Papers 2017/240, International Monetary Fund.
    16. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano, 2021. "Using time-varying volatility for identification in Vector Autoregressions: An application to endogenous uncertainty," Journal of Econometrics, Elsevier, vol. 225(1), pages 47-73.
    17. Gianluca Cubadda & Stefano Grassi & Barbara Guardabascio, 2024. "The Time-Varying Multivariate Autoregressive Index Model," CEIS Research Paper 571, Tor Vergata University, CEIS, revised 10 Jan 2024.
    18. Beckmann, Joscha & Davidson, Sharada Nia & Koop, Gary & Schüssler, Rainer, 2023. "Cross-country uncertainty spillovers: Evidence from international survey data," Journal of International Money and Finance, Elsevier, vol. 130(C).
    19. Paul Labonne, 2022. "Asymmetric Uncertainty: Nowcasting Using Skewness in Real-time Data," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-23, Economic Statistics Centre of Excellence (ESCoE).
    20. Helena Chuliá & Ignacio Garrón & Jorge M. Uribe, 2021. ""Vulnerable Funding in the Global Economy"," IREA Working Papers 202106, University of Barcelona, Research Institute of Applied Economics, revised Mar 2021.
    21. Ogbuabor, Jonathan E. & Ukwueze, Ezebuilo R. & Mba, Ifeoma C. & Ojonta, Obed I. & Orji, Anthony, 2023. "The asymmetric impact of economic policy uncertainty on global retail energy markets: Are the markets responding to the fear of the unknown?," Applied Energy, Elsevier, vol. 334(C).
    22. Giovanni Caggiano & Efrem Castelnuovo, 2023. "Global financial uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 432-449, April.
    23. Graziano Moramarco, 2022. "Measuring Global Macroeconomic Uncertainty and Cross-Country Uncertainty Spillovers," Econometrics, MDPI, vol. 11(1), pages 1-29, December.
    24. Ductor, Lorenzo & Leiva-León, Danilo, 2022. "Fluctuations in global output volatility," Journal of International Money and Finance, Elsevier, vol. 120(C).
    25. Paul Labonne, 2020. "Capturing GDP nowcast uncertainty in real time," Papers 2012.02601, arXiv.org, revised Oct 2021.

  23. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2018. "Endogenous Uncertainty," Working Papers (Old Series) 1805, Federal Reserve Bank of Cleveland.

    Cited by:

    1. Qazi Haque & Leandro M. Magnusson & Kazuki Tomioka, 2019. "Empirical evidence on the dynamics of investment under uncertainty in the U.S," Economics Discussion / Working Papers 19-18, The University of Western Australia, Department of Economics.
    2. Hauzenberger, Niko & Pfarrhofer, Michael & Stelzer, Anna, 2021. "On the effectiveness of the European Central Bank’s conventional and unconventional policies under uncertainty," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 822-845.
    3. Maria Elena Bontempi & Michele Frigeri & Roberto Golinelli & Matteo Squadrani, 2021. "EURQ: A New Web Search‐based Uncertainty Index," Economica, London School of Economics and Political Science, vol. 88(352), pages 969-1015, October.
    4. Gian Paulo Soave, 2020. "International Drivers of Policy Uncertainty in Emerging Economies," Economics Bulletin, AccessEcon, vol. 40(1), pages 716-726.

  24. Carriero, Andrea & Galvao, Ana Beatriz & Marcellino, Massimiliano, 2018. "Credit Conditions and the Asymmetric Effects of Monetary Policy Shocks," EMF Research Papers 17, Economic Modelling and Forecasting Group.

    Cited by:

    1. Martin Bruns & Michele Piffer, 2021. "Monetary policy shocks over the business cycle: Extending the Smooth Transition framework," University of East Anglia School of Economics Working Paper Series 2021-07, School of Economics, University of East Anglia, Norwich, UK..

  25. Foroni, Claudia & Marcellino, Massimiliano & Stevanović, Dalibor, 2018. "Mixed frequency models with MA components," Working Paper Series 2206, European Central Bank.

    Cited by:

    1. Gani Ramadani & Magdalena Petrovska & Vesna Bucevska, 2021. "Evaluation of mixed frequency approaches for tracking near-term economic developments in North Macedonia," Working Papers 2021-03, National Bank of the Republic of North Macedonia.
    2. Ramadani Gani & Petrovska Magdalena & Bucevska Vesna, 2021. "Evaluation of Mixed Frequency Approaches for Tracking Near-Term Economic Developments in North Macedonia," South East European Journal of Economics and Business, Sciendo, vol. 16(2), pages 43-52, December.
    3. Ankargren, Sebastian & Jonéus, Paulina, 2021. "Simulation smoothing for nowcasting with large mixed-frequency VARs," Econometrics and Statistics, Elsevier, vol. 19(C), pages 97-113.
    4. Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers 15114, C.E.P.R. Discussion Papers.

  26. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2018. "The global component of inflation volatility," Temi di discussione (Economic working papers) 1170, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Gianluca Cubadda & Alain Hecq, 2022. "Dimension Reduction for High Dimensional Vector Autoregressive Models," CEIS Research Paper 534, Tor Vergata University, CEIS, revised 24 Mar 2022.
    2. Francesco Corsello & Alex Tagliabracci, 2023. "Assessing the pass-through of energy prices to inflation in the euro area," Questioni di Economia e Finanza (Occasional Papers) 745, Bank of Italy, Economic Research and International Relations Area.
    3. Lodge, David & Pérez, Javier J. & Albrizio, Silvia & Everett, Mary & De Bandt, Olivier & Georgiadis, Georgios & Ca' Zorzi, Michele & Lastauskas, Povilas & Carluccio, Juan & Parrága, Susana & Carvalho,, 2021. "The implications of globalisation for the ECB monetary policy strategy," Occasional Paper Series 263, European Central Bank.
    4. Casalin, Fabrizio & Cerniglia, Floriana & Dia, Enzo, 2023. "Stock-flow adjustments, public debt management and interest costs," Economic Modelling, Elsevier, vol. 129(C).
    5. Efrem Castelnuovo, 2019. "Domestic and Global Uncertainty: A Survey and Some New Results," Melbourne Institute Working Paper Series wp2019n13, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    6. Lorenzo Burlon & Alessandro Notarpietro & Massimiliano Pisani, 2018. "Exchange rate pass-through into euro area inflation. An estimated structural model," Temi di discussione (Economic working papers) 1192, Bank of Italy, Economic Research and International Relations Area.
    7. Ilaria De Angelis & Guido de Blasio & Lucia Rizzica, 2018. "On the unintended effects of public transfers: evidence from EU funding to Southern Italy," Temi di discussione (Economic working papers) 1180, Bank of Italy, Economic Research and International Relations Area.
    8. Stefano Neri & Stefano Siviero, 2019. "The non-standard monetary policy measures of the ECB: motivations, effectiveness and risks," Questioni di Economia e Finanza (Occasional Papers) 486, Bank of Italy, Economic Research and International Relations Area.
    9. Luis J. Álvarez & Ana Gómez-Loscos & María Dolores Gadea, 2019. "Inflation interdependence in advanced economies," Working Papers 1920, Banco de España.
    10. Luis J. Álvarez & Maria Dolores Gadea & Ana Gómez‐Loscos, 2021. "Inflation comovements in advanced economies: Facts and drivers," The World Economy, Wiley Blackwell, vol. 44(2), pages 485-509, February.
    11. Fabio Busetti & Michele Caivano & Davide Delle Monache, 2019. "Domestic and global determinants of inflation: evidence from expectile regression," Temi di discussione (Economic working papers) 1225, Bank of Italy, Economic Research and International Relations Area.
    12. Gianluca Cubadda & Stefano Grassi & Barbara Guardabascio, 2024. "The Time-Varying Multivariate Autoregressive Index Model," CEIS Research Paper 571, Tor Vergata University, CEIS, revised 10 Jan 2024.
    13. İbrahim Özmen & Şerife Özşahin, 2023. "Effects of global energy and price fluctuations on Turkey's inflation: new evidence," Economic Change and Restructuring, Springer, vol. 56(4), pages 2695-2728, August.
    14. Koirala, Niraj P. & Nyiwul, Linus, 2023. "Inflation volatility: A Bayesian approach," Research in Economics, Elsevier, vol. 77(1), pages 185-201.
    15. Martin Feldkircher & Pierre L. Siklos, 2018. "Global inflation dynamics and inflation expectations," CAMA Working Papers 2018-60, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    16. Giovanni Caggiano & Efrem Castelnuovo, 2023. "Global financial uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 432-449, April.
    17. Stefano Neri & Fabio Busetti & Cristina Conflitti & Francesco Corsello & Davide Delle Monache & Alex Tagliabracci, 2023. "Energy price shocks and inflation in the euro area," Questioni di Economia e Finanza (Occasional Papers) 792, Bank of Italy, Economic Research and International Relations Area.

  27. Dario Buono & George Kapetanios & Massimiliano Marcellino & Gianluigi Mazzi & Fotis Papailias, 2018. "Big Data Econometrics: Now Casting and Early Estimates," BAFFI CAREFIN Working Papers 1882, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.

    Cited by:

    1. David Kohns & Arnab Bhattacharjee, 2019. "Interpreting Big Data in the Macro Economy: A Bayesian Mixed Frequency Estimator," CEERP Working Paper Series 010, Centre for Energy Economics Research and Policy, Heriot-Watt University.
    2. George Kapetanios & Fotis Papailias, 2022. "Investigating the predictive ability of ONS big data‐based indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 252-258, March.
    3. Francisco Corona & Graciela Gonz'alez-Far'ias & Jes'us L'opez-P'erez, 2021. "A nowcasting approach to generate timely estimates of Mexican economic activity: An application to the period of COVID-19," Papers 2101.10383, arXiv.org.
    4. Fornaro, Paolo, 2020. "Nowcasting Industrial Production Using Uncoventional Data Sources," ETLA Working Papers 80, The Research Institute of the Finnish Economy.
    5. Irving Fisher Committee, 2023. "Data science in central banking: applications and tools," IFC Bulletins, Bank for International Settlements, number 59.

  28. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2017. "Markov-Switching Three-Pass Regression Filter," Staff Working Papers 17-13, Bank of Canada.

    Cited by:

    1. Elie Bouri & Christina Christou & Rangan Gupta, 2022. "Forecasting Returns of Major Cryptocurrencies: Evidence from Regime-Switching Factor Models," Working Papers 202213, University of Pretoria, Department of Economics.

  29. Marcellino, Massimiliano & Bertolotti, Fabio, 2017. "Tax shocks with high and low uncertainty," CEPR Discussion Papers 12335, C.E.P.R. Discussion Papers.

    Cited by:

    1. Eller, Markus & Hauzenberger, Niko & Huber, Florian & Schuberth, Helene & Vashold, Lukas, 2021. "The impact of macroprudential policies on capital flows in CESEE," Journal of International Money and Finance, Elsevier, vol. 119(C).
    2. Choi, Sangyup & Shin, Junhyeok, 2023. "Household indebtedness and the macroeconomic effects of tax changes," Journal of Economic Behavior & Organization, Elsevier, vol. 209(C), pages 22-52.
    3. K. Peren Arin & Kevin Devereux & Mieszko Mazur, 2021. "Taxes and Firm Investment," Working Papers 202102, School of Economics, University College Dublin.
    4. Jerow, Sam & Wolff, Jonathan, 2022. "Fiscal policy and uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 145(C).

  30. George Kapetanios & Massimiliano Marcellino & Fabrizio Venditti, 2017. "Large time-varying parameter VARs: a non-parametric approach," Temi di discussione (Economic working papers) 1122, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Li, Zheng & Zeng, Jingjing & Hensher, David A., 2023. "An efficient approach to structural breaks and the case of automobile gasoline consumption in Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    2. Tsionas, Mike G. & Izzeldin, Marwan & Trapani, Lorenzo, 2022. "Estimation of large dimensional time varying VARs using copulas," European Economic Review, Elsevier, vol. 141(C).
    3. Petrella, Ivan & Delle Monache, Davide & Venditti, Fabrizio, 2019. "Price Dividend Ratio and Long-Run Stock Returns: a Score Driven State Space Model," CEPR Discussion Papers 14107, C.E.P.R. Discussion Papers.
    4. Allayioti, Anastasia & Venditti, Fabrizio, 2024. "The role of comovement and time-varying dynamics in forecasting commodity prices," Working Paper Series 2901, European Central Bank.
    5. Giacomo Rella, 2021. "The Fed, housing and household debt over time," Department of Economics University of Siena 850, Department of Economics, University of Siena.
    6. Lucchetti, Riccardo & Valentini, Francesco, 2021. "Kernel-based Time-Varying IV estimation: handle with care," MPRA Paper 110033, University Library of Munich, Germany.
    7. Marcellino, Massimiliano & Bai, Yu & Carriero, Andrea & Clark, Todd, 2022. "Macroeconomic Forecasting in a Multi-country Context," CEPR Discussion Papers 16994, C.E.P.R. Discussion Papers.
    8. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
    9. Rajae Azrak & Guy Mélard, 2022. "Autoregressive Models with Time-Dependent Coefficients—A Comparison between Several Approaches," Stats, MDPI, vol. 5(3), pages 1-21, August.
    10. Jiménez-Rodríguez, Rebeca, 2022. "Oil shocks and global economy," Energy Economics, Elsevier, vol. 115(C).
    11. Markus Heinrich & Magnus Reif, 2020. "Real-Time Forecasting Using Mixed-Frequency VARS with Time-Varying Parameters," CESifo Working Paper Series 8054, CESifo.
    12. Jozef Barunik & Michael Ellington, 2020. "Dynamic Network Risk," Papers 2006.04639, arXiv.org, revised Jul 2020.
    13. Diogo de Prince & Emerson Fernandes Marçal & Pedro L. Valls Pereira, 2022. "Forecasting Industrial Production Using Its Aggregated and Disaggregated Series or a Combination of Both: Evidence from One Emerging Market Economy," Econometrics, MDPI, vol. 10(2), pages 1-34, June.
    14. S. Avouyi-Dovi & C. Labonne & R. Lecat & S. Ray, 2017. "Insight from a Time-Varying VAR Model with Stochastic Volatility of the French Housing and Credit Markets," Working papers 620, Banque de France.
    15. Wei, Jie & Zhang, Yonghui, 2020. "A time-varying diffusion index forecasting model," Economics Letters, Elsevier, vol. 193(C).
    16. Mike Tsionas & Marwan Izzeldin & Lorenzo Trapani, 2019. "Bayesian estimation of large dimensional time varying VARs using copulas," Papers 1912.12527, arXiv.org.
    17. Sergei Seleznev, 2019. "Truncated priors for tempered hierarchical Dirichlet process vector autoregression," Bank of Russia Working Paper Series wps47, Bank of Russia.
    18. Gianluca Cubadda & Stefano Grassi & Barbara Guardabascio, 2024. "The Time-Varying Multivariate Autoregressive Index Model," CEIS Research Paper 571, Tor Vergata University, CEIS, revised 10 Jan 2024.
    19. César Castro & Rebeca Jiménez-Rodríguez, 2020. "Dynamic interactions between oil price and exchange rate," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-20, August.
    20. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Apr 2023.

  31. Angela Abbate & Massimiliano Marcellino, 2017. "Macroeconomic activity and risk indicators: an unstable relationship," BAFFI CAREFIN Working Papers 1756, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.

    Cited by:

    1. Francesco Corsello & Valerio Nispi Landi, 2020. "Labor Market and Financial Shocks: A Time‐Varying Analysis," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 52(4), pages 777-801, June.

  32. Claudia Foroni & Pierre Guérin & Massimiliano Marcellino, 2017. "Explaining the Time-varying Effects Of Oil Market Shocks On U.S. Stock Returns," Working Papers 597, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Megaritis, Anastasios & Vlastakis, Nikolaos & Triantafyllou, Athanasios, 2021. "Stock market volatility and jumps in times of uncertainty," Journal of International Money and Finance, Elsevier, vol. 113(C).
    2. Sa Xu & Ziqing Du & Hai Zhang, 2020. "Can Crude Oil Serve as a Hedging Asset for Underlying Securities?—Research on the Heterogenous Correlation between Crude Oil and Stock Index," Energies, MDPI, vol. 13(12), pages 1-19, June.
    3. Alexey Mikhaylov & Ishaq M. Bhatti & Hasan Dinçer & Serhat Yüksel, 2024. "Integrated decision recommendation system using iteration-enhanced collaborative filtering, golden cut bipolar for analyzing the risk-based oil market spillovers," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 305-338, January.
    4. Liu, Zhenhua & Tseng, Hui-Kuan & Wu, Jy S. & Ding, Zhihua, 2020. "Implied volatility relationships between crude oil and the U.S. stock markets: Dynamic correlation and spillover effects," Resources Policy, Elsevier, vol. 66(C).
    5. Liu, Zhenhua & Shi, Xunpeng & Zhai, Pengxiang & Wu, Shan & Ding, Zhihua & Zhou, Yuqin, 2021. "Tail risk connectedness in the oil-stock nexus: Evidence from a novel quantile spillover approach," Resources Policy, Elsevier, vol. 74(C).
    6. Arampatzidis, Ioannis & Panagiotidis, Theodore, 2023. "On the identification of the oil-stock market relationship," Economic Modelling, Elsevier, vol. 120(C).
    7. Mushtaq Hussain Khan & Junaid Ahmed & Mazhar Mughal, 2020. "Oil Price Volatility and Stock Returns: Evidence from Three Oil-price Wars," PIDE-Working Papers 2020:22, Pakistan Institute of Development Economics.
    8. Yousaf, Imran & Beljid, Makram & Chaibi, Anis & Ajlouni, Ahmed AL, 2022. "Do volatility spillover and hedging among GCC stock markets and global factors vary from normal to turbulent periods? Evidence from the global financial crisis and Covid-19 pandemic crisis," Pacific-Basin Finance Journal, Elsevier, vol. 73(C).
    9. Martínez-Cañete, Ana R. & Márquez-de-la-Cruz, Elena & Pérez-Soba, Inés, 2022. "Non-linear cointegration between oil and stock prices: The role of interest rates," Research in International Business and Finance, Elsevier, vol. 59(C).
    10. Arampatzidis, Ioannis & Dergiades, Theologos & Kaufmann, Robert K. & Panagiotidis, Theodore, 2021. "Oil and the U.S. stock market: Implications for low carbon policies," Energy Economics, Elsevier, vol. 103(C).
    11. Diakonova, Marina & Ghirelli, Corinna & Molina, Luis & Pérez, Javier J., 2023. "The economic impact of conflict-related and policy uncertainty shocks: The case of Russia," International Economics, Elsevier, vol. 174(C), pages 69-90.
    12. Ron Alquist & Reinhard Ellwanger & Jianjian Jin, 2020. "The Effect of Oil Price Shocks on Asset Markets: Evidence from Oil Inventory News," Staff Working Papers 2020-8, Bank of Canada.
    13. Eraslan, Sercan & Menla Ali, Faek, 2018. "Oil price shocks and stock return volatility: New evidence based on volatility impulse response analysis," Economics Letters, Elsevier, vol. 172(C), pages 59-62.
    14. Chen, Shiu-Sheng & Huang, Shiangtsz & Lin, Tzu-Yu, 2022. "How do oil prices affect emerging market sovereign bond spreads?," Journal of International Money and Finance, Elsevier, vol. 128(C).
    15. Zhenhua Liu & Zhihua Ding & Tao Lv & Jy S. Wu & Wei Qiang, 2019. "Financial factors affecting oil price change and oil-stock interactions: a review and future perspectives," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 95(1), pages 207-225, January.
    16. Bhaskar Bagchi & Biswajit Paul, 2023. "Effects of Crude Oil Price Shocks on Stock Markets and Currency Exchange Rates in the Context of Russia-Ukraine Conflict: Evidence from G7 Countries," JRFM, MDPI, vol. 16(2), pages 1-18, January.
    17. Mohammad Sharik Essa & Evangelos Giouvris, 2020. "Oil Price, Oil Price Implied Volatility (OVX) and Illiquidity Premiums in the US: (A)symmetry and the Impact of Macroeconomic Factors," JRFM, MDPI, vol. 13(4), pages 1-40, April.
    18. Huang, Wanling & Mollick, Andre Varella, 2020. "Tight oil, real WTI prices and U.S. stock returns," Energy Economics, Elsevier, vol. 85(C).
    19. Le, Thai-Ha & Le, Anh Tu & Le, Ha-Chi, 2021. "The historic oil price fluctuation during the Covid-19 pandemic: What are the causes?," Research in International Business and Finance, Elsevier, vol. 58(C).
    20. Zeina Alsalman, 2021. "Does the source of oil supply shock matter in explaining the behavior of U.S. consumer spending and sentiment?," Empirical Economics, Springer, vol. 61(3), pages 1491-1518, September.
    21. Brice V. Dupoyet & Corey A. Shank, 2018. "Oil prices implied volatility or direction: Which matters more to financial markets?," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(3), pages 275-295, August.
    22. Jiang, Yong & Wang, Gang-Jin & Ma, Chaoqun & Yang, Xiaoguang, 2021. "Do credit conditions matter for the impact of oil price shocks on stock returns? Evidence from a structural threshold VAR model," International Review of Economics & Finance, Elsevier, vol. 72(C), pages 1-15.
    23. Eraslan, Sercan & Ali, Faek Menla, 2018. "Oil price shocks and stock return volatility: New evidence based on volatility impulse response analysis," Discussion Papers 38/2018, Deutsche Bundesbank.

  33. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Measuring Uncertainty and Its Impact on the Economy," BAFFI CAREFIN Working Papers 1639, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.

    Cited by:

    1. Soojin Jo & Rodrigo Sekkel, 2019. "Macroeconomic Uncertainty Through the Lens of Professional Forecasters," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(3), pages 436-446, July.
    2. Fischer, Manfred M. & Huber, Florian & Pfarrhofer, Michael, 2018. "The transmission of uncertainty shocks on income inequality: State-level evidence from the United States," Working Papers in Regional Science 2018/06, WU Vienna University of Economics and Business.
    3. Xianbo Zhou & Zhuoran Chen, 2023. "The Impact of Uncertainty Shocks to Consumption under Different Confidence Regimes Based on a Stochastic Uncertainty-in-Mean TVAR Model," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    4. Gefang, Deborah & Koop, Gary & Poon, Aubrey, 2023. "Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage," International Journal of Forecasting, Elsevier, vol. 39(1), pages 346-363.
    5. Karamysheva, Madina, 2022. "How do fiscal adjustments work? An empirical investigation," Journal of Economic Dynamics and Control, Elsevier, vol. 137(C).
    6. Hernández Vega Marco A., 2021. "The Nonlinear Effect of Uncertainty in Portfolio Flows to Mexico," Working Papers 2021-11, Banco de México.
    7. Deborah Gefang & Gary Koop & Aubrey Poon, 2019. "Variational Bayesian inference in large Vector Autoregressions with hierarchical shrinkage," CAMA Working Papers 2019-08, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    8. Yoosoon Chang & Ana María Herrera & Elena Pesavento, 2023. "Oil prices uncertainty, endogenous regime switching, and inflation anchoring," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(6), pages 820-839, September.
    9. Popiel Michal Ksawery, 2020. "Fiscal policy uncertainty and US output," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(2), pages 1-26, April.
    10. Cross, Jamie L. & Hou, Chenghan & Koop, Gary & Poon, Aubrey, 2023. "Large stochastic volatility in mean VARs," Journal of Econometrics, Elsevier, vol. 236(1).
    11. Efrem Castelnuovo, 2022. "Uncertainty Before and During COVID-19: A Survey," "Marco Fanno" Working Papers 0279, Dipartimento di Scienze Economiche "Marco Fanno".
    12. Luca Rossi, 2020. "Indicators of uncertainty: a brief user’s guide," Questioni di Economia e Finanza (Occasional Papers) 564, Bank of Italy, Economic Research and International Relations Area.
    13. Stefano Giglio & Ian Dew-Becker & David Berger, 2017. "Uncertainty Shocks as Second-Moment News Shocks," 2017 Meeting Papers 403, Society for Economic Dynamics.
    14. González-Sánchez, Mariano & Nave, Juan & Rubio, Gonzalo, 2020. "Effects of uncertainty and risk aversion on the exposure of investment-style factor returns to real activity," Research in International Business and Finance, Elsevier, vol. 53(C).
    15. Sentana, Enrique & Fiorentini, Gabriele, 2018. "Specification tests for non-Gaussian maximum likelihood estimators," CEPR Discussion Papers 12934, C.E.P.R. Discussion Papers.
    16. Ganwen Zheng & Songping Zhu, 2021. "Research on the Effectiveness of China’s Macro Control Policy on Output and Technological Progress under Economic Policy Uncertainty," Sustainability, MDPI, vol. 13(12), pages 1-18, June.
    17. Emmanuel Joel Aikins Abakah & Guglielmo Maria Caporale & Luis A. Gil-Alana, 2020. "Economic Policy Uncertainty: Persistence and Cross-Country Linkages," CESifo Working Paper Series 8289, CESifo.
    18. Bertrand Candelon & Laurent Ferrara & Marc Joëts, 2017. "Global Financial Interconnectedness: A nonlinear Assessment of the Uncertainty Channel," Post-Print hal-01667126, HAL.
    19. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
    20. Aviral Kumar Tiwari & Micheal Kofi Boachie & Rangan Gupta, 2019. "Network Analysis of Economic and Financial Uncertainties in Advanced Economies: Evidence from Graph-Theory," Working Papers 201982, University of Pretoria, Department of Economics.
    21. Kang, Wensheng & Ratti, Ronald. A. & Vespignani, Joaquin, 2018. "Financial and non-financial global stock market volatility shocks," Working Papers 2018-07, University of Tasmania, Tasmanian School of Business and Economics.
    22. Karaki, Mohamad B. & Rangaraju, Sandeep Kumar, 2023. "The confidence channel of U.S. financial uncertainty: Evidence from industry-level data," Economic Modelling, Elsevier, vol. 129(C).
    23. Joshua C.C. Chan & Eric Eisenstat & Chenghan Hou & Gary Koop, 2018. "Composite likelihood methods for large Bayesian VARs with stochastic volatility," CAMA Working Papers 2018-26, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    24. Andrea Carriero & Alessio Volpicella, 2022. "Generalizing the Max Share Identification to multiple shocks identification: an Application to Uncertainty," School of Economics Discussion Papers 0322, School of Economics, University of Surrey.
    25. Kevin Moran & Dalibor Stevanovic & Adam Kader Touré, 2022. "Macroeconomic uncertainty and the COVID‐19 pandemic: Measure and impacts on the Canadian economy," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 55(S1), pages 379-405, February.
    26. Shin, Minchul & Zhang, Boyuan & Zhong, Molin & Lee, Dong Jin, 2018. "Measuring international uncertainty: The case of Korea," Economics Letters, Elsevier, vol. 162(C), pages 22-26.
    27. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2022. "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," CEPR Discussion Papers 17512, C.E.P.R. Discussion Papers.
    28. Danilo Cascaldi-Garcia & Ana Beatriz Galvao, 2018. "News and Uncertainty Shocks," International Finance Discussion Papers 1240, Board of Governors of the Federal Reserve System (U.S.).
    29. Amy Rice & Tugrul Vehbi & Benjamin Wong, 2018. "Measuring uncertainty and its impact on the New Zealand economy," Reserve Bank of New Zealand Analytical Notes series AN2018/01, Reserve Bank of New Zealand.
    30. Miguel Cabello & Rafael Nivin, 2022. "Measuring Uncertainty and its effects in a Small Open Economy," IHEID Working Papers 25-2022, Economics Section, The Graduate Institute of International Studies.
    31. Danilo Leiva-Leon & Luis Uzeda, 2023. "Endogenous Time Variation in Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 105(1), pages 125-142, January.
    32. Danilo Cascaldi-Garcia & Cisil Sarisoy & Juan M. Londono & Bo Sun & Deepa D. Datta & Thiago Ferreira & Olesya Grishchenko & Mohammad R. Jahan-Parvar & Francesca Loria & Sai Ma & Marius Rodriguez & Ilk, 2023. "What Is Certain about Uncertainty?," Journal of Economic Literature, American Economic Association, vol. 61(2), pages 624-654, June.
    33. Zhuo Huang & Fang Liang & Chen Tong, 2021. "The predictive power of macroeconomic uncertainty for commodity futures volatility," International Review of Finance, International Review of Finance Ltd., vol. 21(3), pages 989-1012, September.
    34. Sujoy Mukerji & Han N. Ozsoylev & Jean‐Marc Tallon, 2023. "Trading Ambiguity: A Tale Of Two Heterogeneities," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 1127-1164, August.
    35. Selçuk Gül & Rangan Gupta, 2021. "Time‐varying impact of global, region‐, and country‐specific uncertainties on the volatility of international trade," Contemporary Economic Policy, Western Economic Association International, vol. 39(4), pages 691-700, October.
    36. Costantini, Mauro & Sousa, Ricardo M., 2022. "What uncertainty does to euro area sovereign bond markets: Flight to safety and flight to quality," Journal of International Money and Finance, Elsevier, vol. 122(C).
    37. Julia Darby & Jun Gao & Siobhan Lucey & Sheng Zhu, 2019. "Is heightened political uncertainty priced in stock returns? Evidence from the 2014 Scottish independence referendum," Working Papers 1913, University of Strathclyde Business School, Department of Economics.
    38. Giovanni Caggiano & Efrem Castelnuovo & Juan Manuel Figueres, 2018. "Economic Policy Uncertainty Spillovers in Booms and Busts," CESifo Working Paper Series 7086, CESifo.
    39. Boyan Jovanovic & Sai Ma, 2020. "Uncertainty and Growth Disasters," NBER Working Papers 28024, National Bureau of Economic Research, Inc.
    40. Giovanni Pellegrino & Federico Ravenna & Gabriel Züllig, 2021. "The Impact of Pessimistic Expectations on the Effects of COVID‐19‐Induced Uncertainty in the Euro Area," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(4), pages 841-869, August.
    41. Bucci, Andrea & Palomba, Giulio & Rossi, Eduardo, 2023. "The role of uncertainty in forecasting volatility comovements across stock markets," Economic Modelling, Elsevier, vol. 125(C).
    42. Oscar Claveria, 2020. "Measuring and assessing economic uncertainty," IREA Working Papers 202011, University of Barcelona, Research Institute of Applied Economics, revised Jul 2020.
    43. Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2019. "Vulnerable Growth," American Economic Review, American Economic Association, vol. 109(4), pages 1263-1289, April.
    44. Shang, Fei, 2022. "The effect of uncertainty on the sensitivity of the yield curve to monetary policy surprises," Journal of Economic Dynamics and Control, Elsevier, vol. 137(C).
    45. Ambrogio Cesa-Bianchi & M. Hashem Pesaran & Alessandro Rebucci, 2018. "Uncertainty and Economic Activity: A Multi-Country Perspective," NBER Working Papers 24325, National Bureau of Economic Research, Inc.
    46. Karanasos, M. & Yfanti, S., 2021. "On the Economic fundamentals behind the Dynamic Equicorrelations among Asset classes: Global evidence from Equities, Real estate, and Commodities," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    47. Gorgi, Paolo & Koopman, Siem Jan & Li, Mengheng, 2019. "Forecasting economic time series using score-driven dynamic models with mixed-data sampling," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1735-1747.
    48. Minchul Shin & Molin Zhong, 2020. "A New Approach to Identifying the Real Effects of Uncertainty Shocks," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 367-379, April.
    49. Geert Bekaert & Eric Engstrom & Andrey Ermolov, 2017. "Macro Risks and the Term Structure of Interest Rates," Finance and Economics Discussion Series 2017-058, Board of Governors of the Federal Reserve System (U.S.).
    50. Petar Soric & Oscar Claveria, 2021. "“Employment uncertainty a year after the irruption of the covid-19 pandemic”," AQR Working Papers 202104, University of Barcelona, Regional Quantitative Analysis Group, revised May 2021.
    51. Liu, Pan & Power, Gabriel J. & Vedenov, Dmitry, 2021. "Fair-weather Friends? Sector-specific volatility connectedness and transmission," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 712-736.
    52. Travis J. Berge, 2023. "Time-Varying Uncertainty of the Federal Reserve's Output Gap Estimate," The Review of Economics and Statistics, MIT Press, vol. 105(5), pages 1191-1206, September.
    53. Karin Klieber, 2023. "Non-linear dimension reduction in factor-augmented vector autoregressions," Papers 2309.04821, arXiv.org.
    54. Berger, Tino & Kempa, Bernd & Zou, Feina, 2023. "The role of macroeconomic uncertainty in the determination of the natural rate of interest," Economics Letters, Elsevier, vol. 229(C).
    55. Josué Diwambuena & Jean-Paul K. Tsasa, 2021. "The Real Effects of Uncertainty Shocks: New Evidence from Linear and Nonlinear SVAR Models," BEMPS - Bozen Economics & Management Paper Series BEMPS87, Faculty of Economics and Management at the Free University of Bozen.
    56. Marcellino, Massimiliano & Carriero, Andrea & Corsello, Francesco, 2019. "The Global Component of Inflation Volatility," CEPR Discussion Papers 13470, C.E.P.R. Discussion Papers.
    57. Gupta, Rangan & Ma, Jun & Risse, Marian & Wohar, Mark E., 2018. "Common business cycles and volatilities in US states and MSAs: The role of economic uncertainty," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 317-337.
    58. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Papers 2311.16333, arXiv.org, revised Apr 2024.
    59. Tong, Chen & Huang, Zhuo & Wang, Tianyi & Zhang, Cong, 2023. "The effects of economic uncertainty on financial volatility: A comprehensive investigation," Journal of Empirical Finance, Elsevier, vol. 73(C), pages 369-389.
    60. Alessio Anzuini & Luca Rossi, 2021. "Fiscal policy in the US: a new measure of uncertainty and its effects on the American economy," Empirical Economics, Springer, vol. 61(5), pages 2613-2634, November.
    61. Niko Hauzenberger & Maximilian Bock & Michael Pfarrhofer & Anna Stelzer & Gregor Zens, 2018. "Implications of macroeconomic volatility in the Euro area," Papers 1801.02925, arXiv.org, revised Jun 2018.
    62. Fischer, Manfred M. & Huber, Florian & Pfarrhofer, Michael, 2019. "The regional transmission of uncertainty shocks on income inequality in the United States," Working Papers in Regional Science 2019/01, WU Vienna University of Economics and Business.
    63. Kang, Wensheng & Ratti, Ronald A. & Vespignani, Joaquin L., 2020. "Revising the Impact of Financial and Non-Financial Global Stock Market Volatility Shocks," MPRA Paper 103019, University Library of Munich, Germany.
    64. Jamie L. Cross & Chenghan Hou & Aubrey Poon, 2018. "International Transmission of Macroeconomic Uncertainty in Small Open Economies: An Empirical Approach," Working Papers No 12/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
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    67. M. Karanasos & S. Yfanti & J. Hunter, 2022. "Emerging stock market volatility and economic fundamentals: the importance of US uncertainty spillovers, financial and health crises," Annals of Operations Research, Springer, vol. 313(2), pages 1077-1116, June.
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    71. Jamie L. Cross & Chenghan Hou & Gary Koop, 2021. "Macroeconomic Forecasting with Large Stochastic Volatility in Mean VARs," Working Papers No 04/2021, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
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    76. Takao Asano & Xiaojing Cai & Ryuta Sakemoto, 2023. "Time-varying ambiguity shocks and business cycles," KIER Working Papers 1094, Kyoto University, Institute of Economic Research.
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    83. Fabio Bertolotti & Massimiliano Marcellino, 2019. "Tax shocks with high and low uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 972-993, September.
    84. Piergiorgio Alessandri & Andrea Gazzani & Alejandro Vicondoa, 2021. "The Real Effects of Financial Uncertainty Shocks: A Daily Identification Approach," Documentos de Trabajo 559, Instituto de Economia. Pontificia Universidad Católica de Chile..
    85. Magnus, Jan R. & Pijls, Henk G.J. & Sentana, Enrique, 2021. "The Jacobian of the exponential function," Journal of Economic Dynamics and Control, Elsevier, vol. 127(C).
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    87. Oscar Claveria, 2021. "Uncertainty indicators based on expectations of business and consumer surveys," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 48(2), pages 483-505, May.
    88. Miescu, Mirela S., 2023. "Uncertainty shocks in emerging economies: A global to local approach for identification," European Economic Review, Elsevier, vol. 154(C).
    89. Tosapol Apaitan & Pongsak Luangaram & Pym Manopimoke, 2020. "Uncertainty and Economic Activity: Does it Matter for Thailand?," PIER Discussion Papers 130, Puey Ungphakorn Institute for Economic Research.
    90. Oscar Claveria, 2021. "On the Aggregation of Survey-Based Economic Uncertainty Indicators Between Different Agents and Across Variables," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(1), pages 1-26, April.
    91. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano, 2021. "Using time-varying volatility for identification in Vector Autoregressions: An application to endogenous uncertainty," Journal of Econometrics, Elsevier, vol. 225(1), pages 47-73.
    92. Beckmann, Joscha & Davidson, Sharada Nia & Koop, Gary & Schüssler, Rainer, 2023. "Cross-country uncertainty spillovers: Evidence from international survey data," Journal of International Money and Finance, Elsevier, vol. 130(C).
    93. Evren Erdogan Cosar & Sayg�n Sahinoz, 2018. "Quantifying Uncertainty and Identifying its Impacts on the Turkish Economy," Working Papers 1806, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    94. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    95. Joshua C.C. Chan & Rodney W. Strachan, 2023. "Bayesian State Space Models In Macroeconometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 58-75, February.
    96. Paul Labonne, 2022. "Asymmetric Uncertainty: Nowcasting Using Skewness in Real-time Data," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-23, Economic Statistics Centre of Excellence (ESCoE).
    97. Brianti, Marco, 2021. "Financial Shocks, Uncertainty Shocks, and Monetary Policy Trade-Offs," Working Papers 2021-5, University of Alberta, Department of Economics.
    98. Johnson Worlanyo Ahiadorme, 2022. "On the aggregate effects of global uncertainty: Evidence from an emerging economy," South African Journal of Economics, Economic Society of South Africa, vol. 90(3), pages 390-407, September.
    99. Michael Ryan, 2020. "A Narrative Approach to Creating Instruments with Unstructured and Voluminous Text: An Application to Policy Uncertainty," Working Papers in Economics 20/10, University of Waikato.
    100. Selçuk Gul & Rangan Gupta, 2020. "A Note on the Time-Varying Impact of Global, Region- and Country-Specific Uncertainties on the Volatility of International Trade," Working Papers 202025, University of Pretoria, Department of Economics.
    101. Refk Selmi & Jamal Bouoiyour & Shawkat Hammoudeh, 2020. "Common and country-specific uncertainty fluctuations in oil-producing countries : Measures, macroeconomic effects and policy challenges," Post-Print hal-02929898, HAL.
    102. Heinrich, Markus, 2020. "Does the Current State of the Business Cycle matter for Real-Time Forecasting? A Mixed-Frequency Threshold VAR approach," EconStor Preprints 219312, ZBW - Leibniz Information Centre for Economics.
    103. Hammoudeh, Shawkat & Uddin, Gazi Salah & Sousa, Ricardo M. & Wadström, Christoffer & Sharmi, Rubaiya Zaman, 2022. "Do pandemic, trade policy and world uncertainties affect oil price returns?," Resources Policy, Elsevier, vol. 77(C).
    104. Śmiech, Sławomir & Papież, Monika & Dąbrowski, Marek A., 2019. "How important are different aspects of uncertainty in driving industrial production in the CEE countries?," Research in International Business and Finance, Elsevier, vol. 50(C), pages 252-266.
    105. Cheng, Dong & Shi, Xunpeng & Yu, Jian & Zhang, Dayong, 2019. "How does the Chinese economy react to uncertainty in international crude oil prices?," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 147-164.
    106. Li, Wenhui & Ockenfels, Peter & Wilde, Christian, 2021. "The effect of ambiguity on price formation and trading behavior in financial markets," SAFE Working Paper Series 326, Leibniz Institute for Financial Research SAFE.
    107. OH, Joonseok, 2019. "The propagation of uncertainty shocks : Rotemberg vs. Calvo," Economics Working Papers ECO 2019/01, European University Institute.
    108. Zheng, Hannan & Schwenkler, Gustavo, 2020. "The network of firms implied by the news," ESRB Working Paper Series 108, European Systemic Risk Board.
    109. Tang, Wenjin & Ding, Saijie & Chen, Hao, 2021. "Economic uncertainty and its spillover networks: Evidence from the Asia-Pacific countries," Pacific-Basin Finance Journal, Elsevier, vol. 67(C).
    110. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2020. "The economic drivers of volatility and uncertainty," Temi di discussione (Economic working papers) 1285, Bank of Italy, Economic Research and International Relations Area.
    111. Benjamin K. Johannsen & Elmar Mertens, 2016. "A Time Series Model of Interest Rates With the Effective Lower Bound," Finance and Economics Discussion Series 2016-033, Board of Governors of the Federal Reserve System (U.S.).
    112. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Working Papers 23-04, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Nov 2023.
    113. Ambrocio, Gene, 2020. "Inflationary household uncertainty shocks," Bank of Finland Research Discussion Papers 5/2020, Bank of Finland.
    114. Luo, Jiawen & Marfatia, Hardik A. & Ji, Qiang & Klein, Tony, 2023. "Co-volatility and asymmetric transmission of risks between the global oil and China's futures markets," Energy Economics, Elsevier, vol. 117(C).
    115. Ma, Xiaohan & Samaniego, Roberto, 2019. "Deconstructing uncertainty," European Economic Review, Elsevier, vol. 119(C), pages 22-41.
    116. Cristiana Fiorelli & Alfredo Cartone & Matteo Foglia, 2021. "Shadow rates and spillovers across the Eurozone: a spatial dynamic panel model," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 48(1), pages 223-245, February.
    117. Piergiorgio Alessandri & Andrea Gazzani & Alejandro Vicondoa, 2023. "Are the Effects of Uncertainty Shocks Big or Small?," Working Papers 244, Red Nacional de Investigadores en Economía (RedNIE).
    118. Fuest, Angela & Schmidt, Torsten, 2020. "Inflation expectation uncertainty in a New Keynesian framework," Ruhr Economic Papers 867, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    119. Schüler, Yves S., 2020. "The impact of uncertainty and certainty shocks," Discussion Papers 14/2020, Deutsche Bundesbank.
    120. Myriam Gómez-Méndez & Erwin Hansen, 2021. "Economic policy uncertainty and presidential approval: Evidence from Latin America," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-17, March.
    121. Timo Wollmershäuser & Florian Eckert & Marcell Göttert & Christian Grimme & Carla Krolage & Stefan Lautenbacher & Robert Lehmann & Sebastian Link & Heiner Mikosch & Stefan Neuwirth & Wolfgang Nierhaus, 2019. "ifo Konjunkturprognose Winter 2019: Deutsche Konjunktur stabilisiert sich," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 72(24), pages 27-89, December.
    122. Juan M. Londono & Sai Ma & Beth Anne Wilson, 2021. "The Global Transmission of Real Economic Uncertainty," International Finance Discussion Papers 1317, Board of Governors of the Federal Reserve System (U.S.).
    123. Lin Liu, 2021. "U.S. Economic Uncertainty Shocks and China’s Economic Activities: A Time-Varying Perspective," SAGE Open, , vol. 11(3), pages 21582440211, July.
    124. Bae, Siye & Jo, Soojin & Shim, Myungkyu, 2023. "United States of Mind under Uncertainty," Journal of Economic Behavior & Organization, Elsevier, vol. 213(C), pages 102-127.
    125. Oscar Claveria, 2021. "Disagreement on expectations: firms versus consumers," SN Business & Economics, Springer, vol. 1(12), pages 1-23, December.
    126. Ductor, Lorenzo & Leiva-León, Danilo, 2022. "Fluctuations in global output volatility," Journal of International Money and Finance, Elsevier, vol. 120(C).
    127. Christian Glocker & Werner Hölzl, 2019. "Assessing the Economic Content of Direct and Indirect Business Uncertainty Measures," WIFO Working Papers 576, WIFO.
    128. Paul Labonne, 2020. "Capturing GDP nowcast uncertainty in real time," Papers 2012.02601, arXiv.org, revised Oct 2021.
    129. Guglielmo Maria Caporale & Menelaos Karanasos & Stavroula Yfanti, 2019. "Macro-Financial Linkages in the High-Frequency Domain: The Effects of Uncertainty on Realized Volatility," CESifo Working Paper Series 8000, CESifo.
    130. Dario Caldara & Chiara Scotti & Molin Zhong, 2021. "Macroeconomic and Financial Risks: A Tale of Mean and Volatility," International Finance Discussion Papers 1326, Board of Governors of the Federal Reserve System (U.S.).
    131. Al-Shboul, Mohammad & Assaf, Ata & Mokni, Khaled, 2022. "When bitcoin lost its position: Cryptocurrency uncertainty and the dynamic spillover among cryptocurrencies before and during the COVID-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 83(C).

  34. Dr. Christian Hepenstrick & Massimiliano Marcellino, 2016. "Forecasting with Large Unbalanced Datasets: The Mixed-Frequency Three-Pass Regression Filter," Working Papers 2016-04, Swiss National Bank.

    Cited by:

    1. Hager Ben Romdhane, 2021. "Nowcasting in Tunisia using large datasets and mixed frequency models," IHEID Working Papers 11-2021, Economics Section, The Graduate Institute of International Studies.
    2. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
    3. Dr. Alain Galli & Dr. Christian Hepenstrick & Dr. Rolf Scheufele, 2017. "Mixed-frequency models for tracking short-term economic developments in Switzerland," Working Papers 2017-02, Swiss National Bank.
    4. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2017. "Markov-Switching Three-Pass Regression Filter," Staff Working Papers 17-13, Bank of Canada.
    5. Hagher Ben Rhomdhane & Brahim Mehdi Benlallouna, 2022. "Nowcasting real GDP in Tunisia using large datasets and mixed-frequency models," IHEID Working Papers 02-2022, Economics Section, The Graduate Institute of International Studies.

  35. Marcellino, Massimiliano & Abbate, Angela, 2016. "Point, interval and density forecasts of exchange rates with time-varying parameter models," CEPR Discussion Papers 11559, C.E.P.R. Discussion Papers.

    Cited by:

    1. Angela Abbate & Massimiliano Marcellino, 2017. "Macroeconomic activity and risk indicators: an unstable relationship," BAFFI CAREFIN Working Papers 1756, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    2. Hauzenberger, Niko & Huber, Florian, 2018. "Model instability in predictive exchange rate regressions," Department of Economics Working Paper Series 276, WU Vienna University of Economics and Business.
    3. Papahristodoulou, Christos, 2019. "Is there any theory that explains the SEK?," MPRA Paper 95072, University Library of Munich, Germany, revised 08 Jul 2019.
    4. Anna Pajor & Justyna Wróblewska, 2022. "Forecasting performance of Bayesian VEC-MSF models for financial data in the presence of long-run relationships," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(3), pages 427-448, September.
    5. Oguzhan Cepni & I. Ethem Guney & Norman R. Swanson, 2020. "Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 18-36, January.
    6. Legrand, Romain, 2018. "Time-Varying Vector Autoregressions: Efficient Estimation, Random Inertia and Random Mean," MPRA Paper 88925, University Library of Munich, Germany.
    7. Schüssler, Rainer & Beckmann, Joscha & Koop, Gary & Korobilis, Dimitris, 2018. "Exchange rate predictability and dynamic Bayesian learning," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181523, Verein für Socialpolitik / German Economic Association.
    8. Huber, Florian & Zörner, Thomas O., 2019. "Threshold cointegration in international exchange rates:A Bayesian approach," International Journal of Forecasting, Elsevier, vol. 35(2), pages 458-473.
    9. Adebayo Felix Adekoya & Isaac Kofi Nti & Benjamin Asubam Weyori, 2021. "Long Short-Term Memory Network for Predicting Exchange Rate of the Ghanaian Cedi," FinTech, MDPI, vol. 1(1), pages 1-19, December.
    10. Justyna Wróblewska & Anna Pajor, 2019. "One-period joint forecasts of Polish inflation, unemployment and interest rate using Bayesian VEC-MSF models," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 11(1), pages 23-45, March.
    11. Yemba, Boniface P. & Otunuga, Olusegun Michael & Tang, Biyan & Biswas, Nabaneeta, 2023. "Nowcasting of the Short-run Euro-Dollar Exchange Rate with Economic Fundamentals and Time-varying Parameters," Finance Research Letters, Elsevier, vol. 52(C).
    12. Aristidou, Chrystalleni & Lee, Kevin & Shields, Kalvinder, 2022. "Fundamentals, regimes and exchange rate forecasts: Insights from a meta exchange rate model," Journal of International Money and Finance, Elsevier, vol. 123(C).
    13. Krystian Jaworski, 2021. "Forecasting exchange rates for Central and Eastern European currencies using country‐specific factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 977-999, September.
    14. Camba-Méndez, Gonzalo, 2020. "On the inflation risks embedded in sovereign bond yields," Working Paper Series 2423, European Central Bank.

  36. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Large Vector Autoregressions with Stochastic Volatility and Flexible Priors," Working Papers (Old Series) 1617, Federal Reserve Bank of Cleveland.

    Cited by:

    1. Tallman, Ellis W. & Zaman, Saeed, 2020. "Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy," International Journal of Forecasting, Elsevier, vol. 36(2), pages 373-398.
    2. Brave, Scott A. & Butters, R. Andrew & Justiniano, Alejandro, 2019. "Forecasting economic activity with mixed frequency BVARs," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1692-1707.
    3. Gary Koop & Dimitris Korobilis & Davide Pettenuzzo, 2016. "Bayesian Compressed Vector Autoregressions," Working Papers 103, Brandeis University, Department of Economics and International Business School.
    4. Petrova, Katerina, 2019. "A quasi-Bayesian local likelihood approach to time varying parameter VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 286-306.
    5. Turunen Harry & Zhutova Anastasia & Lemoine Matthieu, 2023. "Stochastic Simulation of the FR-BDF Model and an Assessment of Uncertainty around Conditional Forecasts," Working papers 920, Banque de France.
    6. Eric Eisenstat & Joshua C.C. Chan & Rodney W. Strachan, 2018. "Reducing Dimensions in a Large TVP-VAR," Working Paper Series 43, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    7. Angelini, Elena & Lalik, Magdalena & Lenza, Michele & Paredes, Joan, 2019. "Mind the gap: A multi-country BVAR benchmark for the Eurosystem projections," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1658-1668.
    8. Petrova, Katerina, 2022. "Asymptotically valid Bayesian inference in the presence of distributional misspecification in VAR models," Journal of Econometrics, Elsevier, vol. 230(1), pages 154-182.
    9. Lu, Yunzhi & Li, Jie & Yang, Haisheng, 2023. "Time-varying impacts of monetary policy uncertainty on China's housing market," Economic Modelling, Elsevier, vol. 118(C).
    10. Dellaportas, Petros & Titsias, Michalis K. & Petrova, Katerina & Plataniotis, Anastasios, 2023. "Scalable inference for a full multivariate stochastic volatility model," Journal of Econometrics, Elsevier, vol. 232(2), pages 501-520.
    11. Bognanni, Mark, 2022. "Comment on “Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors”," Journal of Econometrics, Elsevier, vol. 227(2), pages 498-505.
    12. Kohns, David & Potjagailo, Galina, 2023. "Flexible Bayesian MIDAS: time‑variation, group‑shrinkage and sparsity," Bank of England working papers 1025, Bank of England.

  37. Claudia Foroni & Pierre Guérin & Massimiliano Marcellino, 2015. "Using low frequency information for predicting high frequency variables," Working Paper 2015/13, Norges Bank.

    Cited by:

    1. Das, Sonali & Demirer, Riza & Gupta, Rangan & Mangisa, Siphumlile, 2019. "The effect of global crises on stock market correlations: Evidence from scalar regressions via functional data analysis," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 132-147.
    2. Layna Mosley & Victoria Paniagua & Erik Wibbels, 2020. "Moving markets? Government bond investors and microeconomic policy changes," Economics and Politics, Wiley Blackwell, vol. 32(2), pages 197-249, July.
    3. Xu, Qifa & Li, Mengting & Jiang, Cuixia & He, Yaoyao, 2019. "Interconnectedness and systemic risk network of Chinese financial institutions: A LASSO-CoVaR approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    4. Tomás del Barrio Castro & Alain Hecq, 2016. "Testing for Deterministic Seasonality in Mixed-Frequency VARs," DEA Working Papers 76, Universitat de les Illes Balears, Departament d'Economía Aplicada.
    5. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2019. "Forecasting daily electricity prices with monthly macroeconomic variables," Working Paper Series 2250, European Central Bank.
    6. Afees A. Salisu & Rangan Gupta & Riza Demirer, 2021. "Global Financial Cycle and the Predictability of Oil Market Volatility: Evidence from a GARCH-MIDAS Model," Working Papers 202121, University of Pretoria, Department of Economics.
    7. Claudia Foroni & Francesco Ravazzolo & Luca Rossini, 2020. "Are low frequency macroeconomic variables important for high frequency electricity prices?," Papers 2007.13566, arXiv.org, revised Dec 2022.
    8. Bacchiocchi, Emanuele & Bastianin, Andrea & Missale, Alessandro & Rossi, Eduardo, 2020. "Structural analysis with mixed-frequency data: A model of US capital flows," Economic Modelling, Elsevier, vol. 89(C), pages 427-443.
    9. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2017. "Markov-Switching Three-Pass Regression Filter," Staff Working Papers 17-13, Bank of Canada.
    10. Alexander, Carol & Rauch, Johannes, 2021. "A general property for time aggregation," European Journal of Operational Research, Elsevier, vol. 291(2), pages 536-548.
    11. Gaglianone, Wagner Piazza & Marins, Jaqueline Terra Moura, 2017. "Evaluation of exchange rate point and density forecasts: An application to Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 707-728.
    12. Dudda, Tom L. & Klein, Tony & Nguyen, Duc Khuong & Walther, Thomas, 2022. "Common Drivers of Commodity Futures?," QBS Working Paper Series 2022/05, Queen's University Belfast, Queen's Business School.
    13. Xu, Qifa & Chen, Lu & Jiang, Cuixia & Yu, Keming, 2020. "Mixed data sampling expectile regression with applications to measuring financial risk," Economic Modelling, Elsevier, vol. 91(C), pages 469-486.
    14. Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2022. "Mixed‐frequency forecasting of crude oil volatility based on the information content of global economic conditions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 134-157, January.
    15. Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2024. "Forecasting Realized US Stock Market Volatility: Is there a Role for Economic Policy Uncertainty?," Working Papers 202408, University of Pretoria, Department of Economics.
    16. Joel Hasbrouck, 2021. "Rejoinder on: Price Discovery in High Resolution," Journal of Financial Econometrics, Oxford University Press, vol. 19(3), pages 465-471.
    17. Alain Hecq & Marie Ternes & Ines Wilms, 2023. "Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions," Papers 2301.10592, arXiv.org.
    18. Elie Bouri & Rangan Gupta & Luca Rossini, 2022. "The Role of the Monthly ENSO in Forecasting the Daily Baltic Dry Index," Working Papers 202229, University of Pretoria, Department of Economics.
    19. Matteo Bonato & Oguzhan Cepni & Rangan Gupta & Christian Pierdzioch, 2021. "El Nino, La Nina, and Forecastability of the Realized Variance of Agricultural Commodity Prices: Evidence from a Machine Learning Approach," Working Papers 202179, University of Pretoria, Department of Economics.
    20. Xu, Qifa & Zhuo, Xingxuan & Jiang, Cuixia & Liu, Xi & Liu, Yezheng, 2018. "Group penalized unrestricted mixed data sampling model with application to forecasting US GDP growth," Economic Modelling, Elsevier, vol. 75(C), pages 221-236.
    21. Ding, Lili & Zhao, Zhongchao & Wang, Lei, 2022. "Probability density forecasts for natural gas demand in China: Do mixed-frequency dynamic factors matter?," Applied Energy, Elsevier, vol. 312(C).
    22. Çepni, Oğuzhan & Gupta, Rangan & Pienaar, Daniel & Pierdzioch, Christian, 2022. "Forecasting the realized variance of oil-price returns using machine learning: Is there a role for U.S. state-level uncertainty?," Energy Economics, Elsevier, vol. 114(C).
    23. Rangan Gupta & Sarah Nandnaba & Wei Jiang, 2024. "Climate Change and Growth Dynamics," Working Papers 202404, University of Pretoria, Department of Economics.
    24. Le, Trung H., 2020. "Forecasting value at risk and expected shortfall with mixed data sampling," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1362-1379.
    25. Christian Pierdzioch & Rangan Gupta & Hossein Hassani & Emmanuel Silva, 2018. "Forecasting Changes of Economic Inequality: A Boosting Approach," Working Papers 201868, University of Pretoria, Department of Economics.
    26. You, Yu & Liu, Xiaochun, 2020. "Forecasting short-run exchange rate volatility with monetary fundamentals: A GARCH-MIDAS approach," Journal of Banking & Finance, Elsevier, vol. 116(C).
    27. Mahmut Gunay, 2020. "Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial," Working Papers 2002, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    28. Consolo, Agostino & Foroni, Claudia & Martínez Hernández, Catalina, 2021. "A mixed frequency BVAR for the euro area labour market," Working Paper Series 2601, European Central Bank.
    29. Selma Toker & Nimet Özbay & Kristofer Månsson, 2022. "Mixed data sampling regression: Parameter selection of smoothed least squares estimator," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 718-751, July.
    30. Leippold, Markus & Yang, Hanlin, 2019. "Particle filtering, learning, and smoothing for mixed-frequency state-space models," Econometrics and Statistics, Elsevier, vol. 12(C), pages 25-41.

  38. Marcellino, Massimiliano & Kapetanios, George & Carriero, Andrea, 2015. "Structural Analysis with Multivariate Autoregressive Index Models," CEPR Discussion Papers 10801, C.E.P.R. Discussion Papers.

    Cited by:

    1. Gianluca Cubadda & Alain Hecq, 2021. "Reduced Rank Regression Models in Economics and Finance," CEIS Research Paper 525, Tor Vergata University, CEIS, revised 08 Nov 2021.
    2. Gianluca Cubadda & Alain Hecq, 2022. "Dimension Reduction for High Dimensional Vector Autoregressive Models," CEIS Research Paper 534, Tor Vergata University, CEIS, revised 24 Mar 2022.
    3. Gary Koop & Dimitris Korobilis & Davide Pettenuzzo, 2016. "Bayesian Compressed Vector Autoregressions," Working Papers 103, Brandeis University, Department of Economics and International Business School.
    4. Mike G. Tsionas, 2016. "Alternatives to large VAR, VARMA and multivariate stochastic volatility models," Working Papers 217, Bank of Greece.
    5. Marcellino, Massimiliano & Carriero, Andrea & Corsello, Francesco, 2019. "The Global Component of Inflation Volatility," CEPR Discussion Papers 13470, C.E.P.R. Discussion Papers.
    6. Gianluca Cubadda & Barbara Guardabascio, 2017. "Representation, Estimation and Forecasting of the Multivariate Index-Augmented Autoregressive Model," CEIS Research Paper 397, Tor Vergata University, CEIS, revised 13 Jul 2018.
    7. Roberto Casarin & Fausto Corradin & Francesco Ravazzolo & Nguyen Domenico Sartore, 2020. "A Scoring Rule for Factor and Autoregressive Models Under Misspecification," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(2), pages 66-103, June.
    8. Carriero, Andrea & Galvao, Ana Beatriz & Marcellino, Massimiliano, 2018. "Credit Conditions and the Asymmetric Effects of Monetary Policy Shocks," EMF Research Papers 17, Economic Modelling and Forecasting Group.
    9. Gianluca Cubadda & Marco Mazzali, 2023. "The Vector Error Correction Index Model: Representation, Estimation and Identification," CEIS Research Paper 556, Tor Vergata University, CEIS, revised 04 Apr 2023.
    10. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2016. "Structural analysis with Multivariate Autoregressive Index models," Journal of Econometrics, Elsevier, vol. 192(2), pages 332-348.
    11. Gianluca Cubadda & Stefano Grassi & Barbara Guardabascio, 2024. "The Time-Varying Multivariate Autoregressive Index Model," CEIS Research Paper 571, Tor Vergata University, CEIS, revised 10 Jan 2024.
    12. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2020. "The economic drivers of volatility and uncertainty," Temi di discussione (Economic working papers) 1285, Bank of Italy, Economic Research and International Relations Area.
    13. Monica Billio & Roberto Casarin & Sylvia Kaufmann & Matteo Iacopini, 2018. "Bayesian Dynamic Tensor Regression," Working Papers 2018:13, Department of Economics, University of Venice "Ca' Foscari".
    14. Mike G. Tsionas, 2016. "Alternative Bayesian compression in Vector Autoregressions and related models," Working Papers 216, Bank of Greece.

  39. Marcellino, Massimiliano & Sivec, Vasja, 2015. "Monetary, Fiscal and Oil Shocks: Evidence based on Mixed Frequency Structural FAVARs," CEPR Discussion Papers 10610, C.E.P.R. Discussion Papers.

    Cited by:

    1. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2018. "Measuring Uncertainty and Its Impact on the Economy," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 799-815, December.
    2. Simon Beyeler & Sylvia Kaufmann, 2016. "Factor augmented VAR revisited - A sparse dynamic factor model approach," Working Papers 16.08, Swiss National Bank, Study Center Gerzensee.
    3. Jin, Sainan & Miao, Ke & Su, Liangjun, 2021. "On factor models with random missing: EM estimation, inference, and cross validation," Journal of Econometrics, Elsevier, vol. 222(1), pages 745-777.
    4. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    5. Martin Feldkircher & Florian Huber & Michael Pfarrhofer, 2020. "Measuring the Effectiveness of US Monetary Policy during the COVID-19 Recession," Papers 2007.15419, arXiv.org.
    6. Simon Beyeler & Sylvia Kaufmann, 2021. "Reduced‐form factor augmented VAR—Exploiting sparsity to include meaningful factors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(7), pages 989-1012, November.
    7. Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019. "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1263-1272.
    8. Fu Qiao & Yan Yan, 2020. "How does stock market reflect the change in economic demand? A study on the industry-specific volatility spillover networks of China's stock market during the outbreak of COVID-19," Papers 2007.07487, arXiv.org.
    9. Franz Ramsauer & Aleksey Min & Michael Lingauer, 2019. "Estimation of FAVAR Models for Incomplete Data with a Kalman Filter for Factors with Observable Components," Econometrics, MDPI, vol. 7(3), pages 1-43, July.
    10. Si, Deng-Kui & Li, Xiao-Lin & Xu, XuChuan & Fang, Yi, 2021. "The risk spillover effect of the COVID-19 pandemic on energy sector: Evidence from China," Energy Economics, Elsevier, vol. 102(C).
    11. Dhaene, Geert & Wu, Jianbin, 2020. "Incorporating overnight and intraday returns into multivariate GARCH volatility models," Journal of Econometrics, Elsevier, vol. 217(2), pages 471-495.
    12. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.

  40. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2015. "An Overview of the Factor-augmented Error-Correction Model," Discussion Papers 15-03, Department of Economics, University of Birmingham.

    Cited by:

    1. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2021. "Modelling non-stationary ‘Big Data’," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1556-1575.
    2. Tobias Hartl, 2020. "Macroeconomic Forecasting with Fractional Factor Models," Papers 2005.04897, arXiv.org.
    3. Stoupos, Nikolaos & Nikas, Christos & Kiohos, Apostolos, 2023. "Turkey: From a thriving economic past towards a rugged future? - An empirical analysis on the Turkish financial markets," Emerging Markets Review, Elsevier, vol. 54(C).
    4. Francesca Di Iorio & Stefano Fachin, 2017. "Evaluating Restricted Common Factor models for non-stationary data," DSS Empirical Economics and Econometrics Working Papers Series 2017/2, Centre for Empirical Economics and Econometrics, Department of Statistics, "Sapienza" University of Rome.
    5. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.

  41. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Large Vector Autoregressions with Asymmetric Priors," Working Papers 759, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Feldkircher, Martin & Gruber, Thomas & Huber, Florian, 2017. "Spreading the word or reducing the term spread? Assessing spillovers from euro area monetary policy," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168111, Verein für Socialpolitik / German Economic Association.
    2. Gary Koop & Dimitris Korobilis & Davide Pettenuzzo, 2016. "Bayesian Compressed Vector Autoregressions," Working Papers 103, Brandeis University, Department of Economics and International Business School.
    3. Martin Feldkircher & Florian Huber, 2016. "Unconventional US Monetary Policy: New Tools, Same Channels?," Department of Economics Working Papers wuwp222, Vienna University of Economics and Business, Department of Economics.
    4. Feldkircher, Martin & Lukmanova, Elizaveta & Tondl, Gabriele, 2019. "Global Factors Driving Inflation and Monetary Policy: A Global VAR Assessment," Department of Economics Working Paper Series 289, WU Vienna University of Economics and Business.
    5. Joshua Chan & Arnaud Doucet & Roberto Leon-Gonzalez & Rodney W. Strachan, 2018. "Multivariate Stochastic Volatility with Co-Heteroscedasticity," GRIPS Discussion Papers 18-12, National Graduate Institute for Policy Studies.
    6. Pappa, Evi & Molteni, Francesco, 2017. "The Combination of Monetary and Fiscal Policy Shocks: A TVP-FAVAR Approach," CEPR Discussion Papers 12541, C.E.P.R. Discussion Papers.
    7. Gregor Kastner & Florian Huber, 2020. "Sparse Bayesian vector autoregressions in huge dimensions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1142-1165, November.
    8. Emmanuel C. Mamatzakis & Steven Ongena & Mike G. Tsionas, 2023. "The response of household debt to COVID-19 using a neural networks VAR in OECD," Empirical Economics, Springer, vol. 65(1), pages 65-91, July.
    9. Huber, Florian & Zörner, Thomas O., 2019. "Threshold cointegration in international exchange rates:A Bayesian approach," International Journal of Forecasting, Elsevier, vol. 35(2), pages 458-473.
    10. Assaf, A. George & Tsionas, Mike G., 2019. "Forecasting occupancy rate with Bayesian compression methods," Annals of Tourism Research, Elsevier, vol. 75(C), pages 439-449.
    11. Christian Hotz-Behofsits & Florian Huber & Thomas O. Zorner, 2018. "Predicting crypto-currencies using sparse non-Gaussian state space models," Papers 1801.06373, arXiv.org, revised Feb 2018.
    12. Florian Huber & Thomas Zörner, 2017. "Threshold cointegration and adaptive shrinkage," Department of Economics Working Papers wuwp250, Vienna University of Economics and Business, Department of Economics.
    13. Feldkircher, Martin & Gruber, Thomas & Huber, Florian, 2020. "International effects of a compression of euro area yield curves," Journal of Banking & Finance, Elsevier, vol. 113(C).

  42. Stefano Grassi & Tommaso Proietti & Cecilia Frale & Massimiliano Marcellino & Gianluigi Mazzi, 2014. "EuroMInd-C: a Disaggregate Monthly Indicator of Economic Activity for the Euro," Studies in Economics 1406, School of Economics, University of Kent.

    Cited by:

    1. Bisio, Laura & Moauro, Filippo, 2017. "Temporal disaggregation by dynamic regressions: recent developments in Italian quarterly national accounts," MPRA Paper 80211, University Library of Munich, Germany, revised 14 Jul 2017.
    2. Pérez Quirós, Gabriel & Pérez, Javier J. & Paredes, Joan, 2015. "Fiscal targets. A guide to forecasters?," Working Paper Series 1834, European Central Bank.
    3. María Gil & Javier J. Pérez & Alberto Urtasun, 2019. "Nowcasting private consumption: traditional indicators, uncertainty measures, credit cards and some internet data," IFC Bulletins chapters, in: Bank for International Settlements (ed.), The use of big data analytics and artificial intelligence in central banking, volume 50, Bank for International Settlements.

  43. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2014. "Structural FECM: Cointegration in large-scale structural FAVAR models," CEPR Discussion Papers 9858, C.E.P.R. Discussion Papers.

    Cited by:

    1. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    2. Carlo A. Favero & Alessandro Melone, 2019. "Asset Pricing vs Asset Expected Returning in Factor Models," Working Papers 651, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    3. Onatski, A. & Wang, C., 2018. "Extreme canonical correlations and high-dimensional cointegration analysis," Cambridge Working Papers in Economics 1805, Faculty of Economics, University of Cambridge.
    4. Stoupos, Nikolaos & Nikas, Christos & Kiohos, Apostolos, 2023. "Turkey: From a thriving economic past towards a rugged future? - An empirical analysis on the Turkish financial markets," Emerging Markets Review, Elsevier, vol. 54(C).
    5. Barigozzi, Matteo & Lippi, Marco & Luciani, Matteo, 2021. "Large-dimensional Dynamic Factor Models: Estimation of Impulse–Response Functions with I(1) cointegrated factors," Journal of Econometrics, Elsevier, vol. 221(2), pages 455-482.
    6. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2016. "Non-Stationary Dynamic Factor Models for Large Datasets," Finance and Economics Discussion Series 2016-024, Board of Governors of the Federal Reserve System (U.S.).
    7. Kurz-Kim, Jeong-Ryeol, 2018. "A note on the predictive power of survey data in nowcasting euro area GDP," Discussion Papers 10/2018, Deutsche Bundesbank.
    8. Favero, Carlo A. & Melone, Alessandro, 2020. "Asset Pricing vs Asset Expected Returning in Factor-Portfolio Models," CEPR Discussion Papers 14417, C.E.P.R. Discussion Papers.
    9. Matteo Barigozzi & Marco Lippi & Matteo Luciani, 2020. "Cointegration and Error Correction Mechanisms for Singular Stochastic Vectors," Econometrics, MDPI, vol. 8(1), pages 1-23, February.

  44. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2014. "No Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates," CEPR Discussion Papers 9848, C.E.P.R. Discussion Papers.

    Cited by:

    1. Minchul Shin & Molin Zhong, 2015. "Does Realized Volatility Help Bond Yield Density Prediction?," Finance and Economics Discussion Series 2015-115, Board of Governors of the Federal Reserve System (U.S.).
    2. Huber, Florian & Krisztin, Tamás & Piribauer, Philipp, 2014. "Forecasting Global Equity Indices Using Large Bayesian VARs," Department of Economics Working Paper Series 184, WU Vienna University of Economics and Business.
    3. Andrea Renzetti, 2023. "Theory coherent shrinkage of Time-Varying Parameters in VARs," Papers 2311.11858, arXiv.org.
    4. Gregor Bäurle & Daniel Kaufmann, 2018. "Measuring Exchange Rate, Price, and Output Dynamics at the Effective Lower Bound," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 80(6), pages 1243-1266, December.

  45. Knut Are Aastveit & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2014. "Have standard VARs remained stable since the crisis?," Working Paper 2014/13, Norges Bank.

    Cited by:

    1. Delle Monache, Davide & Petrella, Ivan, 2017. "Adaptive models and heavy tails with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 33(2), pages 482-501.
    2. Anastasios Evgenidis & Stephanos Papadamou, 2021. "The impact of unconventional monetary policy in the euro area. Structural and scenario analysis from a Bayesian VAR," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5684-5703, October.
    3. Tallman, Ellis W. & Zaman, Saeed, 2020. "Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy," International Journal of Forecasting, Elsevier, vol. 36(2), pages 373-398.
    4. Niko Hauzenberger & Florian Huber & Massimiliano Marcellino & Nico Petz, 2021. "Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty," Papers 2112.01995, arXiv.org, revised Nov 2022.
    5. Edward S. Knotek & Saeed Zaman, 2017. "Financial Nowcasts and Their Usefulness in Macroeconomic Forecasting," Working Papers (Old Series) 1702, Federal Reserve Bank of Cleveland.
    6. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    7. Rachidi Kotchoni & Dalibor Stevanovic, 2020. "GDP Forecast Accuracy During Recessions," Working Papers 20-06, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    8. Niko Hauzenberger, 2020. "Flexible Mixture Priors for Large Time-varying Parameter Models," Papers 2006.10088, arXiv.org, revised Nov 2020.
    9. Rachidi Kotchoni & Dalibor Stevanovic, 2016. "Forecasting U.S. Recessions and Economic Activity," Working Papers hal-04141569, HAL.
    10. Jarociński, Marek & Bobeica, Elena, 2017. "Missing disinflation and missing inflation: the puzzles that aren't," Working Paper Series 2000, European Central Bank.
    11. Sune Karlsson & Pär Österholm, 2020. "A note on the stability of the Swedish Phillips curve," Empirical Economics, Springer, vol. 59(6), pages 2573-2612, December.
    12. Petrella, Ivan & Antolin-Diaz, Juan & Rubio-Ramírez, Juan Francisco, 2018. "Structural Scenario Analysis with SVARs," CEPR Discussion Papers 12579, C.E.P.R. Discussion Papers.
    13. Todd E. Clark & Michael W. McCracken, 2014. "Evaluating Conditional Forecasts from Vector Autoregressions," Working Papers 2014-25, Federal Reserve Bank of St. Louis.
    14. George Kapetanios & Massimiliano Marcellino & Fabrizio Venditti, 2019. "Large time‐varying parameter VARs: A nonparametric approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1027-1049, November.
    15. Shang, Fei, 2022. "The effect of uncertainty on the sensitivity of the yield curve to monetary policy surprises," Journal of Economic Dynamics and Control, Elsevier, vol. 137(C).
    16. Conti, Antonio M. & Nobili, Andrea & Signoretti, Federico M., 2023. "Bank capital requirement shocks: A narrative perspective," European Economic Review, Elsevier, vol. 151(C).
    17. Bjarni G. Einarsson, 2024. "Online Monitoring of Policy Optimality," Economics wp95, Department of Economics, Central bank of Iceland.
    18. Martin Feldkircher & Nico Hauzenberger, 2019. "How useful are time-varying parameter models for forecasting economic growth in CESEE?," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q1/19, pages 29-48.
    19. Conti, Antonio M., 2021. "Resurrecting the Phillips Curve in Low-Inflation Times," Economic Modelling, Elsevier, vol. 96(C), pages 172-195.
    20. Legrand, Romain, 2018. "Time-Varying Vector Autoregressions: Efficient Estimation, Random Inertia and Random Mean," MPRA Paper 88925, University Library of Munich, Germany.
    21. Karlsson, Sune & Österholm, Pär, 2019. "Volatilities, drifts and the relation between treasury yields and the corporate bond yield spread in australia," Finance Research Letters, Elsevier, vol. 30(C), pages 378-384.
    22. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    23. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2017. "Markov-Switching Three-Pass Regression Filter," Staff Working Papers 17-13, Bank of Canada.
    24. Edvinsson, Rodney & Karlsson, Sune & Österholm, Pär, 2023. "Does Money Growth Predict Inflation? Evidence from Vector Autoregressions Using Four Centuries of Data," Working Papers 2023:3, Örebro University, School of Business.
    25. Markus Heinrich & Magnus Reif, 2020. "Real-Time Forecasting Using Mixed-Frequency VARS with Time-Varying Parameters," CESifo Working Paper Series 8054, CESifo.
    26. Neville Francis & Laura E. Jackson & Michael T. Owyang, 2014. "How Has Empirical Monetary Policy Analysis Changed After the Financial Crisis?," Working Papers 2014-19, Federal Reserve Bank of St. Louis.
    27. Hacioglu Hoke, Sinem, 2019. "Macroeconomic effects of political risk shocks," Bank of England working papers 841, Bank of England.
    28. Kenichiro McAlinn & Knut Are Aastveit & Jouchi Nakajima & Mike West, 2019. "Multivariate Bayesian Predictive Synthesis in Macroeconomic Forecasting," Working Paper 2019/2, Norges Bank.
    29. Orkideh Gharehgozli & Sunhyung Lee, 2022. "Money Supply and Inflation after COVID-19," Economies, MDPI, vol. 10(5), pages 1-14, April.
    30. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    31. Ganics, Gergely & Odendahl, Florens, 2021. "Bayesian VAR forecasts, survey information, and structural change in the euro area," International Journal of Forecasting, Elsevier, vol. 37(2), pages 971-999.
    32. Kavanagh, Ella & Zhu, Sheng & O’Sullivan, Niall, 2022. "Monetary policy, trade-offs and the transmission of UK Monetary Policy," Journal of Policy Modeling, Elsevier, vol. 44(6), pages 1128-1147.
    33. Heinrich, Markus, 2020. "Does the Current State of the Business Cycle matter for Real-Time Forecasting? A Mixed-Frequency Threshold VAR approach," EconStor Preprints 219312, ZBW - Leibniz Information Centre for Economics.
    34. Aymeric Ortmans, 2020. "Evolving Monetary Policy in the Aftermath of the Great Recession," Documents de recherche 20-01, Centre d'Études des Politiques Économiques (EPEE), Université d'Evry Val d'Essonne.
    35. Arnaud Dufays & Zhuo Li & Jeroen V.K. Rombouts & Yong Song, 2021. "Sparse change‐point VAR models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 703-727, September.
    36. Benjamin Garcia & Arsenios Skaperdas, 2017. "Inferring the Shadow Rate from Real Activity," Finance and Economics Discussion Series 2017-106, Board of Governors of the Federal Reserve System (U.S.).

  46. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.

    Cited by:

    1. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    2. Markus Heinrich & Magnus Reif, 2018. "Forecasting using mixed-frequency VARs with time-varying parameters," ifo Working Paper Series 273, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    3. Michael W. McCracken & Michael T. Owyang & Tatevik Sekhposyan, 2021. "Real-Time Forecasting and Scenario Analysis Using a Large Mixed-Frequency Bayesian VAR," International Journal of Central Banking, International Journal of Central Banking, vol. 17(71), pages 1-41, December.
    4. Lenza, Michele & Cimadomo, Jacopo & Giannone, Domenico & Monti, Francesca & Sokol, Andrej, 2021. "Nowcasting with Large Bayesian Vector Autoregressions," CEPR Discussion Papers 15854, C.E.P.R. Discussion Papers.
    5. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2019. "Forecasting daily electricity prices with monthly macroeconomic variables," Working Paper Series 2250, European Central Bank.
    6. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    7. Guy P. Nason & Ben Powell & Duncan Elliott & Paul A. Smith, 2017. "Should we sample a time series more frequently?: decision support via multirate spectrum estimation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(2), pages 353-407, February.
    8. Thomas B. Götz & Alain W. Hecq, 2019. "Granger Causality Testing in Mixed‐Frequency VARs with Possibly (Co)Integrated Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 914-935, November.
    9. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2014. "Combined Density Nowcasting in an uncertain economic environment," Working Paper 2014/17, Norges Bank.
    10. Claudia Foroni & Francesco Ravazzolo & Luca Rossini, 2020. "Are low frequency macroeconomic variables important for high frequency electricity prices?," Papers 2007.13566, arXiv.org, revised Dec 2022.
    11. Markus Heinrich & Magnus Reif, 2020. "Real-Time Forecasting Using Mixed-Frequency VARS with Time-Varying Parameters," CESifo Working Paper Series 8054, CESifo.
    12. Blasques, F. & Koopman, S.J. & Mallee, M. & Zhang, Z., 2016. "Weighted maximum likelihood for dynamic factor analysis and forecasting with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 405-417.
    13. Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019. "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1263-1272.
    14. Jian Chai & Puju Cao & Xiaoyang Zhou & Kin Keung Lai & Xiaofeng Chen & Siping (Sue) Su, 2018. "The Conductive and Predictive Effect of Oil Price Fluctuations on China’s Industry Development Based on Mixed-Frequency Data," Energies, MDPI, vol. 11(6), pages 1-14, May.
    15. Heinrich, Markus, 2020. "Does the Current State of the Business Cycle matter for Real-Time Forecasting? A Mixed-Frequency Threshold VAR approach," EconStor Preprints 219312, ZBW - Leibniz Information Centre for Economics.

  47. Claudia Foroni & Massimiliano Marcellino, 2014. "Mixed frequency structural VARs," Working Paper 2014/01, Norges Bank.

    Cited by:

    1. Marcellino, Massimiliano & Sivec, Vasja, 2016. "Monetary, fiscal and oil shocks: Evidence based on mixed frequency structural FAVARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 335-348.
    2. Laurent Ferrara & Pierre Guérin, 2018. "What are the macroeconomic effects of high-frequency uncertainty shocks?," Post-Print hal-02334586, HAL.
    3. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    4. Chambers, Marcus J., 2016. "The estimation of continuous time models with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 390-404.
    5. Skrobotov, Anton (Скроботов, Антон) & Turuntseva, Marina (Турунцева, Марина), 2015. "Theoretical Aspects of Modeling of the SVAR [Теоретические Аспекты Моделирования Svar]," Published Papers mak8, Russian Presidential Academy of National Economy and Public Administration.
    6. Bent Jesper Christensen & Olaf Posch & Michel van der Wel, 2014. "Estimating Dynamic Equilibrium Models Using Mixed Frequency Macro and Financial Data," CESifo Working Paper Series 5030, CESifo.

  48. Claudia Foroni & Massimiliano Marcellino, 2013. "Mixed frequency structural models: estimation, and policy analysis," Working Paper 2013/15, Norges Bank.

    Cited by:

    1. Canova, Fabio & Bluwstein, Kristina, 2015. "Beggar-thy-neighbor? The international effects of ECB unconventional monetary policy measures," CEPR Discussion Papers 10856, C.E.P.R. Discussion Papers.
    2. Giannone, Domenico & Monti, Francesca & Reichlin, Lucrezia, 2014. "Exploiting the monthly data-flow in structural forecasting," LSE Research Online Documents on Economics 57998, London School of Economics and Political Science, LSE Library.
    3. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2015. "Testing for Granger causality in large mixed-frequency VARs," Discussion Papers 45/2015, Deutsche Bundesbank.
    4. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.

  49. Yuliya Rychalovska & Massimiliano Marcellino (EUI), 2013. "An estimated DSGE model of a Small Open Economy within the Monetary Union: Forecasting and Structural Analysis," EcoMod2013 5302, EcoMod.

    Cited by:

    1. Bettendorf, Timo, 2013. "Feeding the Global VAR with theory: Is German wage moderation to blame for European imbalances?," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79710, Verein für Socialpolitik / German Economic Association.
    2. Massimiliano Marcellino & Yuliya Rychalovska, 2014. "Forecasting with a DSGE Model of a Small Open Economy within the Monetary Union," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(5), pages 315-338, August.

  50. Massimiliano Marcellino & Mario Porqueddu & Fabrizio Venditti, 2013. "Short-term GDP forecasting with a mixed frequency dynamic factor model with stochastic volatility," Temi di discussione (Economic working papers) 896, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    2. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2018. "Measuring Uncertainty and Its Impact on the Economy," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 799-815, December.
    3. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    4. Libero Monteforte & Valentina Raponi, 2019. "Short‐term forecasts of economic activity: Are fortnightly factors useful?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(3), pages 207-221, April.
    5. Allayioti, Anastasia & Venditti, Fabrizio, 2024. "The role of comovement and time-varying dynamics in forecasting commodity prices," Working Paper Series 2901, European Central Bank.
    6. Luiz Renato Lima & Lucas Lúcio Godeiro & Mohammed Mohsin, 2021. "Time-Varying Dictionary and the Predictive Power of FED Minutes," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 149-181, January.
    7. Reichlin, Lucrezia & Giannone, Domenico & Modugno, Michele & Banbura, Marta, 2012. "Now-casting and the real-time data flow," CEPR Discussion Papers 9112, C.E.P.R. Discussion Papers.
    8. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    9. Ana Arencibia Pareja & Ana Gomez-Loscos & Mercedes de Luis López & Gabriel Perez-Quiros, 2020. "A Short Term Forecasting Model for the Spanish GDP and itsDemand Components," Revista Economía, Fondo Editorial - Pontificia Universidad Católica del Perú, vol. 43(85), pages 1-30.
    10. Guido Bulligan & Fabio Busetti & Michele Caivano & Pietro Cova & Davide Fantino & Alberto Locarno & Lisa Rodano, 2017. "The Bank of Italy econometric model: an update of the main equations and model elasticities," Temi di discussione (Economic working papers) 1130, Bank of Italy, Economic Research and International Relations Area.
    11. Matteo Iacopini & Francesco Ravazzolo & Luca Rossini, 2022. "Bayesian Multivariate Quantile Regression with alternative Time-varying Volatility Specifications," Papers 2211.16121, arXiv.org.
    12. Antolin-Diaz, Juan & Drechsel, Thomas & Petrella, Ivan, 2017. "Tracking the slowdown in long-run GDP growth," LSE Research Online Documents on Economics 81869, London School of Economics and Political Science, LSE Library.
    13. Christian Glocker & Philipp Wegmüller, 2017. "Business Cycle Dating and Forecasting with Real-time Swiss GDP Data," WIFO Working Papers 542, WIFO.
    14. Antonello D’Agostino & Jacopo Cimadomo, 2015. "Combining time-variation and mixed-frequencies: an analysis of government spending multipliers in Italy," Working Papers 7, European Stability Mechanism.
    15. Catherine Doz & Laurent Ferrara & Pierre-Alain Pionnier, 2020. "Business cycle dynamics after the Great Recession: An Extended Markov-Switching Dynamic Factor Model," Working Papers halshs-02443364, HAL.
    16. Ahiadorme, Johnson Worlanyo, 2020. "Monetary policy transmission and income inequality in Sub-Saharan Africa," MPRA Paper 104084, University Library of Munich, Germany.
    17. Petrella, Ivan & Drechsel, Thomas & Antolin-Diaz, Juan, 2014. "Following the Trend: Tracking GDP when Long-Run Growth is Uncertain," CEPR Discussion Papers 10272, C.E.P.R. Discussion Papers.
    18. Fumio Hayashi & Yuta Tachi, 2023. "Nowcasting Japan’s GDP," Empirical Economics, Springer, vol. 64(4), pages 1699-1735, April.
    19. Leif Anders Thorsrud, 2016. "Nowcasting using news topics. Big Data versus big bank," Working Paper 2016/20, Norges Bank.
    20. Han Liu & Yongjing Wang & Haiyan Song & Ying Liu, 2023. "Measuring tourism demand nowcasting performance using a monotonicity test," Tourism Economics, , vol. 29(5), pages 1302-1327, August.
    21. Davide Delle Monache & Ivan Petrella & Fabrizio Venditti, 2016. "Common Faith or Parting Ways? A Time Varying Parameters Factor Analysis of Euro-Area Inflation," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 539-565, Emerald Group Publishing Limited.
    22. Boriss Siliverstovs, 2019. "Assessing Nowcast Accuracy of US GDP Growth in Real Time: The Role of Booms and Busts," Working Papers 2019/01, Latvijas Banka.
    23. Li, Chenxing & Maheu, John M & Yang, Qiao, 2022. "An Infinite Hidden Markov Model with Stochastic Volatility," MPRA Paper 115456, University Library of Munich, Germany.
    24. Marek Rusnak, 2013. "Nowcasting Czech GDP in Real Time," Working Papers 2013/06, Czech National Bank.
    25. Ankargren, Sebastian & Jonéus, Paulina, 2021. "Simulation smoothing for nowcasting with large mixed-frequency VARs," Econometrics and Statistics, Elsevier, vol. 19(C), pages 97-113.
    26. Michal Franta & David Havrlant & Marek Rusnak, 2014. "Forecasting Czech GDP Using Mixed-Frequency Data Models," Working Papers 2014/08, Czech National Bank.
    27. Vegard H�ghaug Larsen & Leif Anders Thorsrud, 2018. "Business cycle narratives," Working Papers No 6/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    28. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
    29. Valentina Aprigliano & Alessandro Borin & Francesco Paolo Conteduca & Simone Emiliozzi & Marco Flaccadoro & Sabina Marchetti & Stefania Villa, 2021. "Forecasting Italian GDP growth with epidemiological data," Questioni di Economia e Finanza (Occasional Papers) 664, Bank of Italy, Economic Research and International Relations Area.
    30. Davide Pettenuzzo & Rossen Valkanov & Allan Timmermann, 2014. "A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics," Working Papers 76, Brandeis University, Department of Economics and International Business School.
    31. Jonas Krampe & Luca Margaritella, 2021. "Factor Models with Sparse VAR Idiosyncratic Components," Papers 2112.07149, arXiv.org, revised May 2022.
    32. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2016. "A MIDAS approach to modeling first and second moment dynamics," Journal of Econometrics, Elsevier, vol. 193(2), pages 315-334.
    33. Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019. "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1263-1272.
    34. Carriero, Andrea & Mumtaz, Haroon & Theophilopoulou, Angeliki, 2015. "Macroeconomic information, structural change, and the prediction of fiscal aggregates," International Journal of Forecasting, Elsevier, vol. 31(2), pages 325-348.
    35. Leif Anders Thorsrud, 2016. "Words are the new numbers: A newsy coincident index of business cycles," Working Papers No 4/2016, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    36. Paul Labonne, 2022. "Asymmetric Uncertainty: Nowcasting Using Skewness in Real-time Data," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-23, Economic Statistics Centre of Excellence (ESCoE).
    37. Hauber, Philipp, 2022. "Real-time nowcasting with sparse factor models," EconStor Preprints 251551, ZBW - Leibniz Information Centre for Economics.
    38. Dhaene, Geert & Wu, Jianbin, 2020. "Incorporating overnight and intraday returns into multivariate GARCH volatility models," Journal of Econometrics, Elsevier, vol. 217(2), pages 471-495.
    39. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    40. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2020. "The economic drivers of volatility and uncertainty," Temi di discussione (Economic working papers) 1285, Bank of Italy, Economic Research and International Relations Area.
    41. Cem Cakmakli & Hamza Demircan, 2020. "Using Survey Information for Improving the Density Nowcasting of US GDP with a Focus on Predictive Performance during Covid-19 Pandemic," Koç University-TUSIAD Economic Research Forum Working Papers 2016, Koc University-TUSIAD Economic Research Forum.
    42. Manu García & Juan F. Rubio-Ramírez, 2019. "Now-casting Spain," Working Papers 2019-03, FEDEA.
    43. Eraslan, Sercan & Schröder, Maximilian, 2023. "Nowcasting GDP with a pool of factor models and a fast estimation algorithm," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1460-1476.
    44. Sebastian Ankargren & Paulina Jon'eus, 2019. "Estimating Large Mixed-Frequency Bayesian VAR Models," Papers 1912.02231, arXiv.org.
    45. Robert Lehmann & Magnus Reif & Timo Wollmershäuser, 2020. "ifoCAST: The New Forecast Standard of the ifo Institute," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 73(11), pages 31-39, November.
    46. Ademmer, Martin & Boysen-Hogrefe, Jens & Fiedler, Salomon & Groll, Dominik & Hauber, Philipp & Jannsen, Nils & Kooths, Stefan & Potjagailo, Galina, 2018. "Deutsche Konjunktur im Frühjahr 2018 - Deutsche Wirtschaft näher am Limit [German Economy Spring 2018 - German economy closer to its limit]," Kieler Konjunkturberichte 41, Kiel Institute for the World Economy (IfW Kiel).
    47. Leippold, Markus & Yang, Hanlin, 2019. "Particle filtering, learning, and smoothing for mixed-frequency state-space models," Econometrics and Statistics, Elsevier, vol. 12(C), pages 25-41.

  51. Cecilia Frale & Stefano Grassi & Massimiliano Marcellino & Gianluigi Mazzi & Tommaso Proietti, 2013. "EuroMInd-C: a Disaggregate Monthly Indicator of Economic Activity for the Euro Area and member countries," CEIS Research Paper 287, Tor Vergata University, CEIS, revised 01 Oct 2013.

    Cited by:

    1. Jaime Martínez-Martín & Elena Rusticelli, 2020. "Keeping track of global trade in real time," Working Papers 2019, Banco de España.
    2. Paul Labonne & Martin Weale, 2020. "Temporal disaggregation of overlapping noisy quarterly data: estimation of monthly output from UK value‐added tax data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1211-1230, June.
    3. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    4. Bisio, Laura & Moauro, Filippo, 2017. "Temporal disaggregation by dynamic regressions: recent developments in Italian quarterly national accounts," MPRA Paper 80211, University Library of Munich, Germany, revised 14 Jul 2017.
    5. Katja Heinisch & Rolf Scheufele, 2018. "Bottom-up or direct? Forecasting German GDP in a data-rich environment," Empirical Economics, Springer, vol. 54(2), pages 705-745, March.
    6. Luke Mosley & Tak-Shing Chan & Alex Gibberd, 2023. "sparseDFM: An R Package to Estimate Dynamic Factor Models with Sparse Loadings," Papers 2303.14125, arXiv.org.
    7. Pérez Quirós, Gabriel & Pérez, Javier J. & Paredes, Joan, 2015. "Fiscal targets. A guide to forecasters?," Working Paper Series 1834, European Central Bank.
    8. Pinkwart, Nicolas, 2018. "Short-term forecasting economic activity in Germany: A supply and demand side system of bridge equations," Discussion Papers 36/2018, Deutsche Bundesbank.

  52. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2013. "Real-Time Nowcasting with a Bayesian Mixed Frequency Model with Stochastic Volatility," CEPR Discussion Papers 9312, C.E.P.R. Discussion Papers.

    Cited by:

    1. Galvão, Ana Beatriz & Giraitis, Liudas & Kapetanios, George & Petrova, Katerina, 2016. "A time varying DSGE model with financial frictions," Journal of Empirical Finance, Elsevier, vol. 38(PB), pages 690-716.
    2. Soojin Jo & Rodrigo Sekkel, 2019. "Macroeconomic Uncertainty Through the Lens of Professional Forecasters," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(3), pages 436-446, July.
    3. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    4. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    5. Markus Heinrich & Magnus Reif, 2018. "Forecasting using mixed-frequency VARs with time-varying parameters," ifo Working Paper Series 273, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    6. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
    7. Marcellino, Massimiliano & Clark, Todd & Carriero, Andrea, 2021. "Nowcasting Tail Risk to Economic Activity at a Weekly Frequency," CEPR Discussion Papers 16496, C.E.P.R. Discussion Papers.
    8. Luiz Renato Lima & Lucas Lúcio Godeiro & Mohammed Mohsin, 2021. "Time-Varying Dictionary and the Predictive Power of FED Minutes," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 149-181, January.
    9. Reichlin, Lucrezia & Giannone, Domenico & Modugno, Michele & Banbura, Marta, 2012. "Now-casting and the real-time data flow," CEPR Discussion Papers 9112, C.E.P.R. Discussion Papers.
    10. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    11. Matteo Mogliani & Anna Simoni, 2020. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Post-Print hal-03089878, HAL.
    12. Michael W. McCracken & Michael T. Owyang & Tatevik Sekhposyan, 2021. "Real-Time Forecasting and Scenario Analysis Using a Large Mixed-Frequency Bayesian VAR," International Journal of Central Banking, International Journal of Central Banking, vol. 17(71), pages 1-41, December.
    13. David Kohns & Arnab Bhattacharjee, 2020. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," Papers 2011.00938, arXiv.org, revised May 2022.
    14. David Kohns & Arnab Bhattacharjee, 2019. "Interpreting Big Data in the Macro Economy: A Bayesian Mixed Frequency Estimator," CEERP Working Paper Series 010, Centre for Energy Economics Research and Policy, Heriot-Watt University.
    15. Ana Beatriz Galvão & Michael Owyang, 2022. "Forecasting low‐frequency macroeconomic events with high‐frequency data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1314-1333, November.
    16. Antonello D’Agostino & Jacopo Cimadomo, 2015. "Combining time-variation and mixed-frequencies: an analysis of government spending multipliers in Italy," Working Papers 7, European Stability Mechanism.
    17. Martin Feldkircher & Florian Huber & Michael Pfarrhofer, 2020. "Measuring the Effectiveness of US Monetary Policy during the COVID-19 Recession," Papers 2007.15419, arXiv.org.
    18. Lenza, Michele & Cimadomo, Jacopo & Giannone, Domenico & Monti, Francesca & Sokol, Andrej, 2021. "Nowcasting with Large Bayesian Vector Autoregressions," CEPR Discussion Papers 15854, C.E.P.R. Discussion Papers.
    19. Fabian Kr�ger & Todd E. Clark & Francesco Ravazzolo, 2015. "Using Entropic Tilting to Combine BVAR Forecasts with External Nowcasts," Working Papers No 8/2015, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    20. Boriss Siliverstovs, 2021. "New York FED Staff Nowcasts and Reality: What Can We Learn about the Future, the Present, and the Past?," Econometrics, MDPI, vol. 9(1), pages 1-25, March.
    21. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2019. "Forecasting daily electricity prices with monthly macroeconomic variables," Working Paper Series 2250, European Central Bank.
    22. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
    23. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    24. Deborah Gefang & Gary Koop & Aubrey Poon, 2020. "Computationally Efficient Inference in Large Bayesian Mixed Frequency VARs," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2020-07, Economic Statistics Centre of Excellence (ESCoE).
    25. Robert M. Kunst & Martin Wagner, 2020. "Economic forecasting: editors’ introduction," Empirical Economics, Springer, vol. 58(1), pages 1-5, January.
    26. Soybilgen, Barış & Yazgan, Ege, 2018. "Evaluating nowcasts of bridge equations with advanced combination schemes for the Turkish unemployment rate," Economic Modelling, Elsevier, vol. 72(C), pages 99-108.
    27. Ankargren Sebastian & Unosson Måns & Yang Yukai, 2020. "A Flexible Mixed-Frequency Vector Autoregression with a Steady-State Prior," Journal of Time Series Econometrics, De Gruyter, vol. 12(2), pages 1-41, July.
    28. Knotek, Edward S. & Zaman, Saeed, 2023. "Real-time density nowcasts of US inflation: A model combination approach," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1736-1760.
    29. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2014. "Combined Density Nowcasting in an uncertain economic environment," Working Paper 2014/17, Norges Bank.
    30. Claudia Foroni & Francesco Ravazzolo & Luca Rossini, 2020. "Are low frequency macroeconomic variables important for high frequency electricity prices?," Papers 2007.13566, arXiv.org, revised Dec 2022.
    31. Marcellino, Massimiliano & Clark, Todd & Huber, Florian & Koop, Gary & Pfarrhofer, Michael, 2022. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," CEPR Discussion Papers 17461, C.E.P.R. Discussion Papers.
    32. Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
    33. Garciga, Christian & Knotek II, Edward S., 2019. "Forecasting GDP growth with NIPA aggregates: In search of core GDP," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1814-1828.
    34. Boriss Siliverstovs, 2019. "Assessing Nowcast Accuracy of US GDP Growth in Real Time: The Role of Booms and Busts," Working Papers 2019/01, Latvijas Banka.
    35. Berg, Tim Oliver & Henzel, Steffen, 2013. "Point and Density Forecasts for the Euro Area Using Many Predictors: Are Large BVARs Really Superior?," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79783, Verein für Socialpolitik / German Economic Association.
    36. Shrub, Yuliya & Rieger, Jonas & Müller, Henrik & Jentsch, Carsten, 2022. "Text data rule - don't they? A study on the (additional) information of Handelsblatt data for nowcasting German GDP in comparison to established economic indicators," Ruhr Economic Papers 964, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    37. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2014. "Density forecasts with MIDAS models," Working Paper 2014/10, Norges Bank.
    38. Marek Rusnak, 2013. "Nowcasting Czech GDP in Real Time," Working Papers 2013/06, Czech National Bank.
    39. Markus Heinrich & Magnus Reif, 2020. "Real-Time Forecasting Using Mixed-Frequency VARS with Time-Varying Parameters," CESifo Working Paper Series 8054, CESifo.
    40. Frédérique Bec & Matteo Mogliani, 2013. "Nowcasting French GDP in Real-Time from Survey Opinions : Information or Forecast Combinations ?," Working Papers 2013-21, Center for Research in Economics and Statistics.
    41. Tamás Kiss & Stepan Mazur & Hoang Nguyen & Pär Österholm, 2023. "Modeling the relation between the US real economy and the corporate bond‐yield spread in Bayesian VARs with non‐Gaussian innovations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 347-368, March.
    42. Edward S. Knotek & Saeed Zaman, 2017. "Nowcasting U.S. Headline and Core Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(5), pages 931-968, August.
    43. Kenichiro McAlinn, 2021. "Mixed‐frequency Bayesian predictive synthesis for economic nowcasting," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1143-1163, November.
    44. Matteo Mogliani & Anna Simoni, 2024. "Bayesian Bi-level Sparse Group Regressions for Macroeconomic Forecasting," Papers 2404.02671, arXiv.org.
    45. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2016. "A MIDAS approach to modeling first and second moment dynamics," Journal of Econometrics, Elsevier, vol. 193(2), pages 315-334.
    46. Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019. "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1263-1272.
    47. Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers 15114, C.E.P.R. Discussion Papers.
    48. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    49. Knut Are Aastveit & Tuva Marie Fastbø & Eleonora Granziera & Kenneth Sæterhagen Paulsen & Kjersti Næss Torstensen, 2020. "Nowcasting Norwegian household consumption with debit card transaction data," Working Paper 2020/17, Norges Bank.
    50. Carriero, Andrea & Mumtaz, Haroon & Theophilopoulou, Angeliki, 2015. "Macroeconomic information, structural change, and the prediction of fiscal aggregates," International Journal of Forecasting, Elsevier, vol. 31(2), pages 325-348.
    51. Jos Jansen, W. & Jin, Xiaowen & Winter, Jasper M. de, 2016. "Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts," Munich Reprints in Economics 43488, University of Munich, Department of Economics.
    52. Heinrich, Markus, 2020. "Does the Current State of the Business Cycle matter for Real-Time Forecasting? A Mixed-Frequency Threshold VAR approach," EconStor Preprints 219312, ZBW - Leibniz Information Centre for Economics.
    53. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    54. Kohns, David & Bhattacharjee, Arnab, 2023. "Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1384-1412.
    55. Cem Cakmakli & Hamza Demircan, 2020. "Using Survey Information for Improving the Density Nowcasting of US GDP with a Focus on Predictive Performance during Covid-19 Pandemic," Koç University-TUSIAD Economic Research Forum Working Papers 2016, Koc University-TUSIAD Economic Research Forum.
    56. Sebastian Ankargren & Paulina Jon'eus, 2019. "Estimating Large Mixed-Frequency Bayesian VAR Models," Papers 1912.02231, arXiv.org.
    57. George Athanasopoulos & Puwasala Gamakumara & Anastasios Panagiotelis & Rob J Hyndman & Mohamed Affan, 2019. "Hierarchical Forecasting," Monash Econometrics and Business Statistics Working Papers 2/19, Monash University, Department of Econometrics and Business Statistics.
    58. Boriss Siliverstovs, 2021. "Gauging the Effect of Influential Observations on Measures of Relative Forecast Accuracy in a Post-COVID-19 Era: Application to Nowcasting Euro Area GDP Growth," Working Papers 2021/01, Latvijas Banka.
    59. Gary Koop & Stuart McIntyre & James Mitchell, 2020. "UK regional nowcasting using a mixed frequency vector auto‐regressive model with entropic tilting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 91-119, January.
    60. Tim Oliver Berg & Steffen Henzel, 2014. "Point and Density Forecasts for the Euro Area Using Bayesian VARs," CESifo Working Paper Series 4711, CESifo.
    61. Kohns, David & Potjagailo, Galina, 2023. "Flexible Bayesian MIDAS: time‑variation, group‑shrinkage and sparsity," Bank of England working papers 1025, Bank of England.
    62. González-Astudillo, Manuel & Baquero, Daniel, 2019. "A nowcasting model for Ecuador: Implementing a time-varying mean output growth," Economic Modelling, Elsevier, vol. 82(C), pages 250-263.
    63. Dario Caldara & Chiara Scotti & Molin Zhong, 2021. "Macroeconomic and Financial Risks: A Tale of Mean and Volatility," International Finance Discussion Papers 1326, Board of Governors of the Federal Reserve System (U.S.).

  53. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.

    Cited by:

    1. Aliocha Accardo & Sylvérie Herbert & Cristina Jude & Adrian Penalver, 2023. "Measuring and Comparing Consumption Inequality between France and the United States," Working papers 904, Banque de France.
    2. Chudik, Alexander & Georgiadis, Georgios, 2019. "Estimation of impulse response functions when shocks are observed at a higher frequency than outcome variables," Working Paper Series 2307, European Central Bank.
    3. Prabheesh, K.P. & Sasongko, Aryo & Indawan, Fiskara, 2023. "Did the policy responses influence credit and business cycle co-movement during the COVID-19 crisis? Evidence from Indonesia," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 243-255.
    4. Özer Karagedikli & Murat Özbilgin, 2019. "Mixed in New Zealand: Nowcasting Labour Markets with MIDAS," Reserve Bank of New Zealand Analytical Notes series AN2019/04, Reserve Bank of New Zealand.
    5. Markus Heinrich & Magnus Reif, 2018. "Forecasting using mixed-frequency VARs with time-varying parameters," ifo Working Paper Series 273, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    6. Kaustubh & Soumya Bhadury & Saurabh Ghosh, 2024. "Reinvigorating Gva Nowcasting In The Postpandemic Period: A Case Study For India," Bulletin of Monetary Economics and Banking, Bank Indonesia, vol. 27(Spesial I), pages 95-130, Februari.
    7. Fady Barsoum, 2015. "Point and Density Forecasts Using an Unrestricted Mixed-Frequency VAR Model," Working Paper Series of the Department of Economics, University of Konstanz 2015-19, Department of Economics, University of Konstanz.
    8. Das, Sonali & Demirer, Riza & Gupta, Rangan & Mangisa, Siphumlile, 2019. "The effect of global crises on stock market correlations: Evidence from scalar regressions via functional data analysis," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 132-147.
    9. Sarun Kamolthip, 2021. "Macroeconomic forecasting with LSTM and mixed frequency time series data," Papers 2109.13777, arXiv.org.
    10. Jin, Sainan & Miao, Ke & Su, Liangjun, 2021. "On factor models with random missing: EM estimation, inference, and cross validation," Journal of Econometrics, Elsevier, vol. 222(1), pages 745-777.
    11. Heiner Mikosch & Laura Solanko, 2019. "Forecasting Quarterly Russian GDP Growth with Mixed-Frequency Data," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 19-35, March.
    12. Antonello D’Agostino & Jacopo Cimadomo, 2015. "Combining time-variation and mixed-frequencies: an analysis of government spending multipliers in Italy," Working Papers 7, European Stability Mechanism.
    13. Ahiadorme, Johnson Worlanyo, 2020. "Monetary policy transmission and income inequality in Sub-Saharan Africa," MPRA Paper 104084, University Library of Munich, Germany.
    14. Jung, Alexander, 2017. "Forecasting broad money velocity," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 421-432.
    15. João B. Assunção & Pedro Afonso Fernandes, 2022. "Nowcasting GDP: An Application to Portugal," Forecasting, MDPI, vol. 4(3), pages 1-15, August.
    16. Daniel L. Millimet & Ian K. McDonough, 2017. "Dynamic Panel Data Models With Irregular Spacing: With an Application to Early Childhood Development," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(4), pages 725-743, June.
    17. Staehr, Karsten & Vermeulen, Robert, 2016. "How competitiveness shocks affect macroeconomic performance across euro area countries," Working Paper Series 1940, European Central Bank.
    18. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    19. Kertlly de Medeiros, Rennan & da Nóbrega Besarria, Cássio & Pitta de Jesus, Diego & Phillipe de Albuquerquemello, Vinicius, 2022. "Forecasting oil prices: New approaches," Energy, Elsevier, vol. 238(PC).
    20. Ankargren Sebastian & Unosson Måns & Yang Yukai, 2020. "A Flexible Mixed-Frequency Vector Autoregression with a Steady-State Prior," Journal of Time Series Econometrics, De Gruyter, vol. 12(2), pages 1-41, July.
    21. Gani Ramadani & Magdalena Petrovska & Vesna Bucevska, 2021. "Evaluation of mixed frequency approaches for tracking near-term economic developments in North Macedonia," Working Papers 2021-03, National Bank of the Republic of North Macedonia.
    22. Löchel, H. & Packham, N. & Walisch, F., 2016. "Determinants of the onshore and offshore Chinese government yield curves," Pacific-Basin Finance Journal, Elsevier, vol. 36(C), pages 77-93.
    23. Guy P. Nason & Ben Powell & Duncan Elliott & Paul A. Smith, 2017. "Should we sample a time series more frequently?: decision support via multirate spectrum estimation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(2), pages 353-407, February.
    24. Grant Allan & Gary Koop & Stuart McIntyre & Paul Smith, 2019. "Nowcasting Using Mixed Frequency Methods: An Application to the Scottish Economy," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 12-45, September.
    25. Santiago Etchegaray Alvarez, 2022. "Proyecciones macroeconómicas con datos en frecuencias mixtas. Modelos ADL-MIDAS, U-MIDAS y TF-MIDAS con aplicaciones para Uruguay," Documentos de trabajo 2022004, Banco Central del Uruguay.
    26. Marianna Cervená & Martin Schneider, 2010. "Short-term forecasting GDP with a DSGE model augmented by monthly indicators," Working Papers 163, Oesterreichische Nationalbank (Austrian Central Bank).
    27. Chambers, Marcus J., 2016. "The estimation of continuous time models with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 390-404.
    28. Boriss Siliverstovs, 2019. "Assessing Nowcast Accuracy of US GDP Growth in Real Time: The Role of Booms and Busts," Working Papers 2019/01, Latvijas Banka.
    29. Lynda Khalaf & Maral Kichian & Charles Saunders & Marcel Voia, 2021. "Dynamic panels with MIDAS covariates: Nonlinearity, estimation and fit," Post-Print hal-03528880, HAL.
    30. Foroni, Claudia & Marcellino, Massimiliano & Stevanović, Dalibor, 2018. "Mixed frequency models with MA components," Discussion Papers 02/2018, Deutsche Bundesbank.
    31. Deistler, Manfred & Koelbl, Lukas & Anderson, Brian D.O., 2017. "Non-identifiability of VMA and VARMA systems in the mixed frequency case," Econometrics and Statistics, Elsevier, vol. 4(C), pages 31-38.
    32. Marek Rusnak, 2013. "Nowcasting Czech GDP in Real Time," Working Papers 2013/06, Czech National Bank.
    33. Michal Franta & David Havrlant & Marek Rusnak, 2014. "Forecasting Czech GDP Using Mixed-Frequency Data Models," Working Papers 2014/08, Czech National Bank.
    34. Smith Paul, 2016. "Nowcasting UK GDP during the depression," Working Papers 1606, University of Strathclyde Business School, Department of Economics.
    35. Deschamps, Bruno & Ioannidis, Christos & Ka, Kook, 2020. "High-frequency credit spread information and macroeconomic forecast revision," International Journal of Forecasting, Elsevier, vol. 36(2), pages 358-372.
    36. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    37. Emilian DOBRESCU, 2020. "Self-fulfillment degree of economic expectations within an integrated space: The European Union case study," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 5-32, December.
    38. Frömmel, Michael & Midiliç, Murat, 2021. "Daily currency interventions in an emerging market: Incorporating reserve accumulation to the reaction function," Economic Modelling, Elsevier, vol. 97(C), pages 461-476.
    39. Vegard H�ghaug Larsen & Leif Anders Thorsrud, 2018. "Business cycle narratives," Working Papers No 6/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    40. Afees A. Salisu & Rangan Gupta, 2019. "How do Housing Returns in Emerging Countries Respond to Oil Shocks? A MIDAS Touch," Working Papers 201946, University of Pretoria, Department of Economics.
    41. Hale, Galina & Lopez, Jose A., 2019. "Monitoring banking system connectedness with big data," Journal of Econometrics, Elsevier, vol. 212(1), pages 203-220.
    42. Asger Lunde & Miha Torkar, 2020. "Including news data in forecasting macro economic performance of China," Computational Management Science, Springer, vol. 17(4), pages 585-611, December.
    43. Xianning WANG & Jingrong DONG & Zhi XIAO & Guanjie HE, 2019. "A novel spatial mixed frequency forecasting model with application to Chinese regional GDP," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 54-77, June.
    44. Maas, Daniel & Mayer, Eric & Rüth, Sebastian, 2015. "Current account dynamics and the housing boom and bust cycle in Spain," W.E.P. - Würzburg Economic Papers 94, University of Würzburg, Department of Economics.
    45. Bahar Şen Doğan & Murat Midiliç, 2019. "Forecasting Turkish real GDP growth in a data-rich environment," Empirical Economics, Springer, vol. 56(1), pages 367-395, January.
    46. Leif Anders Thorsrud, 2016. "Words are the new numbers: A newsy coincident index of business cycles," Working Papers No 4/2016, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    47. Bonino-Gayoso, Nicolás & García-Hiernaux, Alfredo, 2019. "TF-MIDAS: a new mixed-frequency model to forecast macroeconomic variables," MPRA Paper 93366, University Library of Munich, Germany.
    48. Freitag L., 2014. "Default probabilities, CDS premiums and downgrades : A probit-MIDAS analysis," Research Memorandum 038, Maastricht University, Graduate School of Business and Economics (GSBE).
    49. Raffaele Mattera & Michelangelo Misuraca & Maria Spano & Germana Scepi, 2023. "Mixed frequency composite indicators for measuring public sentiment in the EU," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2357-2382, June.
    50. Allan, Grant & Koop, Gary & McIntyre, Stuart & Smith, Paul, 2014. "Nowcasting Scottish GDP Growth," SIRE Discussion Papers 2015-08, Scottish Institute for Research in Economics (SIRE).
    51. Clements, Michael P. & Galvão, Ana Beatriz, 2017. "Model and survey estimates of the term structure of US macroeconomic uncertainty," International Journal of Forecasting, Elsevier, vol. 33(3), pages 591-604.
    52. Heinrich, Markus, 2020. "Does the Current State of the Business Cycle matter for Real-Time Forecasting? A Mixed-Frequency Threshold VAR approach," EconStor Preprints 219312, ZBW - Leibniz Information Centre for Economics.
    53. Salisu, Afees A. & Gupta, Rangan, 2021. "Oil shocks and stock market volatility of the BRICS: A GARCH-MIDAS approach," Global Finance Journal, Elsevier, vol. 48(C).
    54. Tony Chernis & Rodrigo Sekkel, 2018. "Nowcasting Canadian Economic Activity in an Uncertain Environment," Discussion Papers 18-9, Bank of Canada.
    55. Sylvia Kaufmann, 2023. "Covid-19 outbreak and beyond: a retrospect on the information content of short-time workers for GDP now- and forecasting," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-10, December.
    56. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    57. Yun Liu & Yeonwoo Rho, 2018. "On the Choice of Instruments in Mixed Frequency Specification Tests," Papers 1809.05503, arXiv.org.
    58. Marie Bessec, 2015. "Revisiting the transitional dynamics of business-cycle phases with mixed frequency data," Post-Print hal-01276824, HAL.
    59. Barış Soybilgen & M. Ege Yazgan & Hüseyin Kaya, 2023. "Nowcasting Turkish Food Inflation Using Daily Online Prices," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(2), pages 171-190, September.
    60. Heiner Mikosch & Ying Zhang, 2014. "Forecasting Chinese GDP Growth with Mixed Frequency Data," KOF Working papers 14-359, KOF Swiss Economic Institute, ETH Zurich.
    61. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    62. Ghysels, Eric & Qian, Hang, 2019. "Estimating MIDAS regressions via OLS with polynomial parameter profiling," Econometrics and Statistics, Elsevier, vol. 9(C), pages 1-16.
    63. Eugen Scarlat, 2016. "Connectivity - Based Clustering of GDP Time Series," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 23-38, March.
    64. Wichitaksorn, Nuttanan, 2022. "Analyzing and forecasting Thai macroeconomic data using mixed-frequency approach," Journal of Asian Economics, Elsevier, vol. 78(C).
    65. Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.
    66. Trujillo-Barrera, Andres & Pennings, Joost M.E., 2013. "Energy and Food Commodity Prices Linkage: An Examination with Mixed-Frequency Data," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150465, Agricultural and Applied Economics Association.
    67. Emilio Blanco & Fiorella Dogliolo & Lorena Garegnani, 2022. "Nowcasting during the Pandemic: Lessons from Argentina," BCRA Working Paper Series 202299, Central Bank of Argentina, Economic Research Department.

  54. Marcellino, Massimiliano & Eickmeier, Sandra & Prieto, Esteban, 2013. "Time Variation in Macro-Financial Linkages," CEPR Discussion Papers 9436, C.E.P.R. Discussion Papers.

    Cited by:

    1. Dr. Angela Abbate & Sandra Eickmeier & Esteban Prieto, 2020. "Financial shocks and inflation dynamics," Working Papers 2020-13, Swiss National Bank.
    2. Andrea Silvestrini & Andrea Zaghini, 2015. "Financial Shocks And The Real Economy In A Nonlinear World: From Theory To Estimation," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 15/910, Ghent University, Faculty of Economics and Business Administration.
    3. Angela Abbate & Massimiliano Marcellino, 2017. "Macroeconomic activity and risk indicators: an unstable relationship," BAFFI CAREFIN Working Papers 1756, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    4. Salzmann, Leonard, 2020. "The Impact of Uncertainty and Financial Shocks in Recessions and Booms," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224588, Verein für Socialpolitik / German Economic Association.
    5. Cornand, Camille & Gandré, Pauline & Gimet, Céline, 2016. "Increase in home bias in the Eurozone debt crisis: The role of domestic shocks," Economic Modelling, Elsevier, vol. 53(C), pages 445-469.
    6. Andrea Silvestrini & Andrea Zaghini, 2015. "Financial shocks and the real economy in a nonlinear world: a survey of the theoretical and empirical literature," Questioni di Economia e Finanza (Occasional Papers) 255, Bank of Italy, Economic Research and International Relations Area.
    7. Megaritis, Anastasios & Vlastakis, Nikolaos & Triantafyllou, Athanasios, 2021. "Stock market volatility and jumps in times of uncertainty," Journal of International Money and Finance, Elsevier, vol. 113(C).
    8. Davidson, Sharada Nia & Moccero, Diego Nicolas, 2024. "The nonlinear effects of banks’ vulnerability to capital depletion in euro area countries," Working Paper Series 2912, European Central Bank.
    9. Eickmeier, Sandra & Metiu, Norbert & Prieto, Esteban, 2016. "Time-varying Volatility, Financial Intermediation and Monetary Policy," IWH Discussion Papers 19/2016, Halle Institute for Economic Research (IWH).
    10. Francesco Furlanetto & Francesco Ravazzolo & Samad Sarferaz, 2014. "Identification of financial factors in economic fluctuations," KOF Working papers 14-364, KOF Swiss Economic Institute, ETH Zurich.
    11. Antonio M. Conti & Stefano Neri & Andrea Nobili, 2015. "Why is inflation so low in the euro area?," Temi di discussione (Economic working papers) 1019, Bank of Italy, Economic Research and International Relations Area.
    12. Antonio M. Conti & Andrea Nobili & Federico M. Signoretti, 2018. "Bank capital constraints, lending supply and economic activity," Temi di discussione (Economic working papers) 1199, Bank of Italy, Economic Research and International Relations Area.
    13. Kiss, Tamás & Nguyen, Hoang & Österholm, Pär, 2022. "The Relation between the High-Yield Bond Spread and the Unemployment Rate in the Euro Area," Finance Research Letters, Elsevier, vol. 46(PA).
    14. Knut Are Aastveit & Francesco Furlanetto & Francesca Loria, 2023. "Has the Fed Responded to House and Stock Prices? A Time-Varying Analysis," The Review of Economics and Statistics, MIT Press, vol. 105(5), pages 1314-1324, September.
    15. Conti, Antonio M. & Nobili, Andrea & Signoretti, Federico M., 2023. "Bank capital requirement shocks: A narrative perspective," European Economic Review, Elsevier, vol. 151(C).
    16. John Cotter & Mark Hallam & Kamil Yilmaz, 2020. "Macro-Financial Spillovers," Working Papers 202005, Geary Institute, University College Dublin.
    17. Kiss, Tamás & Österholm, Pär, 2020. "Fat tails in leading indicators," Economics Letters, Elsevier, vol. 193(C).
    18. Leroy, Aurélien & Pop, Adrian, 2019. "Macro-financial linkages: The role of the institutional framework," Journal of International Money and Finance, Elsevier, vol. 92(C), pages 75-97.
    19. Bijsterbosch, Martin & Falagiarda, Matteo, 2015. "The macroeconomic impact of financial fragmentation in the euro area: Which role for credit supply?," Journal of International Money and Finance, Elsevier, vol. 54(C), pages 93-115.
    20. Baumann, Ursel & Lodge, David & Miescu, Mirela S., 2019. "Global growth on life support? The contributions of fiscal and monetary policy since the global financial crisis," Working Paper Series 2248, European Central Bank.
    21. Zhang, Wen, 2019. "Deciphering the causes for the post-1990 slow output recoveries," Economics Letters, Elsevier, vol. 176(C), pages 28-34.
    22. Tamás Kiss & Stepan Mazur & Hoang Nguyen & Pär Österholm, 2023. "Modeling the relation between the US real economy and the corporate bond‐yield spread in Bayesian VARs with non‐Gaussian innovations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 347-368, March.
    23. Ellington, Michael & Florackis, Chris & Milas, Costas, 2017. "Liquidity shocks and real GDP growth: Evidence from a Bayesian time-varying parameter VAR," Journal of International Money and Finance, Elsevier, vol. 72(C), pages 93-117.
    24. Domenico Giannone & Michele Lenza & Lucrezia Reichlin, 2019. "Money, Credit, Monetary Policy, and the Business Cycle in the Euro Area: What Has Changed Since the Crisis?," International Journal of Central Banking, International Journal of Central Banking, vol. 15(5), pages 137-173, December.
    25. Stijn Claessens & M Ayhan Kose, 2018. "Frontiers of macrofinancial linkages," BIS Papers, Bank for International Settlements, number 95.
    26. Cheng, Chak Hung Jack & Chiu, Ching-Wai (Jeremy), 2016. "Nonlinearities of mortgage spreads over the business cycles," Bank of England working papers 634, Bank of England.
    27. Ellington, Michael, 2018. "Financial market illiquidity shocks and macroeconomic dynamics: Evidence from the UK," Journal of Banking & Finance, Elsevier, vol. 89(C), pages 225-236.
    28. Camille Cornand & Pauline Gandré, 2013. "Home bias and self-fulfilling sovereign debt crisis," Post-Print halshs-00861603, HAL.
    29. Karlsson, Sune & Österholm, Pär, 2019. "The Relation between the Corporate Bond-Yield Spread and the Real Economy: Stable or TimeVarying?," Working Papers 2019:7, Örebro University, School of Business.
    30. Hilberg, Björn & Grill, Michael & Metiu, Norbert, 2016. "Credit constraints and the international propagation of US financial shocks," Working Paper Series 1954, European Central Bank.
    31. Carrillo Julio A. & García Ana Laura, 2021. "The COVID-19 Economic Crisis in Mexico through the Lens of a Financial Conditions Index," Working Papers 2021-23, Banco de México.
    32. Francesco Corsello & Valerio Nispi Landi, 2020. "Labor Market and Financial Shocks: A Time‐Varying Analysis," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 52(4), pages 777-801, June.
    33. Marcus Ingholt, 2018. "LTV vs. DTI Constraints: When Did They Bind, and How Do They Interact?," 2018 Meeting Papers 866, Society for Economic Dynamics.
    34. Hosszú, Zsuzsanna, 2018. "The impact of credit supply shocks and a new Financial Conditions Index based on a FAVAR approach," Economic Systems, Elsevier, vol. 42(1), pages 32-44.
    35. Karlsson, Sune & Österholm, Pär, 2020. "A hybrid time-varying parameter Bayesian VAR analysis of Okun’s law in the United States," Economics Letters, Elsevier, vol. 197(C).
    36. Neri, Stefano & Nobili, Andrea & Conti, Antonio M., 2017. "Low inflation and monetary policy in the euro area," Working Paper Series 2005, European Central Bank.
    37. Muhammad Zeshan & Wasim Shahid Malik & Muhammad Nasir, 2019. "Oil Price Shocks, Systematic Monetary Policy and Economic Activity," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 58(1), pages 65-81.
    38. Narcissa Balta & Bořek Vašíček, 2020. "Financial channels and economic activity in the euro area: a large-scale Bayesian VAR approach," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 47(2), pages 431-451, May.
    39. Giovanni Dell'Ariccia & Karl Habermeier & Vikram Haksar & Tommaso Mancini-Griffoli, 2017. "Monetary Policy and Financial Stability," RBA Annual Conference Volume (Discontinued), in: Jonathan Hambur & John Simon (ed.),Monetary Policy and Financial Stability in a World of Low Interest Rates, Reserve Bank of Australia.
    40. Liu, Tangyong & Gong, Xu, 2020. "Analyzing time-varying volatility spillovers between the crude oil markets using a new method," Energy Economics, Elsevier, vol. 87(C).
    41. Haroon Mumtaz & Konstantinos Theodoridis, 2016. "Volatility Co-movement and the Great Moderation. An Empirical Analysis," Working Papers 804, Queen Mary University of London, School of Economics and Finance.
    42. Paolo Gorgi & Siem Jan Koopman & Julia Schaumburg, 2021. "Vector Autoregressions with Dynamic Factor Coefficients and Conditionally Heteroskedastic Errors," Tinbergen Institute Discussion Papers 21-056/III, Tinbergen Institute.
    43. Salzmann, Leonard, 2019. "The Impact of Uncertainty and Financial Shocks in Recessions and Booms," EconStor Preprints 206691, ZBW - Leibniz Information Centre for Economics.
    44. Martin Harding & Rafael Wouters, 2022. "Risk and State-Dependent Financial Frictions," Staff Working Papers 22-37, Bank of Canada.

  55. Eric Ghysels & Pierre Guérin & Massimiliano Marcellino, 2013. "Regime Switches in the Risk-Return Trade-Off," Staff Working Papers 13-51, Bank of Canada.

    Cited by:

    1. Chotipong Charoensom, 2024. "An Estimation of Regime Switching Models with Nonlinear Endogenous Switching," PIER Discussion Papers 217, Puey Ungphakorn Institute for Economic Research.
    2. Suzanne G. M. Fifield & David G. McMillan & Fiona J. McMillan, 2020. "Is there a risk and return relation?," The European Journal of Finance, Taylor & Francis Journals, vol. 26(11), pages 1075-1101, July.
    3. Nektarios Aslanidis & Charlotte Christiansen, 2017. "Flight to Safety from European Stock Markets," CREATES Research Papers 2017-38, Department of Economics and Business Economics, Aarhus University.
    4. Yun Xie & Yixiang Tian & Zhuang Xiao & Xiangyun Zhou, 2018. "Dependence of credit spread and macro-conditions based on an alterable structure model," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-15, May.
    5. Jyri Kinnunen & Minna Martikainen, 2017. "Dynamic Autocorrelation and International Portfolio Allocation," Multinational Finance Journal, Multinational Finance Journal, vol. 21(1), pages 21-48, March.
    6. Miguel A. Duran, 2024. "The Risk-Return Relation in the Corporate Loan Market," Papers 2401.12315, arXiv.org.
    7. John Cotter & Enrique Salvador, 2022. "The non-linear trade-off between return and risk and its determinants," Working Papers 202203, Geary Institute, University College Dublin.
    8. Tobias Adrian & Richard K. Crump & Erik Vogt, 2019. "Nonlinearity and Flight‐to‐Safety in the Risk‐Return Trade‐Off for Stocks and Bonds," Journal of Finance, American Finance Association, vol. 74(4), pages 1931-1973, August.
    9. Rachidi Kotchoni, 2018. "Detecting and Measuring Nonlinearity," Econometrics, MDPI, vol. 6(3), pages 1-27, August.
    10. Hossein Asgharian & Charlotte Christiansen & Ai Jun Hou & Weining Wang, 2017. "Long- and Short-Run Components of Factor Betas: Implications for Equity Pricing," CREATES Research Papers 2017-34, Department of Economics and Business Economics, Aarhus University.
    11. Byrne, Joseph P. & Sakemoto, Ryuta, 2021. "The conditional volatility premium on currency portfolios," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    12. Joseph, Byrne & Sakemoto, Ryuta, 2020. "The Conditional Risk and Return Trade-Off on Currency Portfolios," MPRA Paper 99497, University Library of Munich, Germany.
    13. Amanjot Singh & Manjit Singh, 2016. "Risk–Return Relationship in BRIC Equity Markets: Evidence from Markov Regime Switching Model with Time-varying Transition Probabilities," Metamorphosis: A Journal of Management Research, , vol. 15(2), pages 69-78, December.
    14. Naqi Shah, Sadia & Qayyum, Abdul, 2016. "Analyse Risk-Return Paradox: Evidence from Electricity Sector of Pakistan," MPRA Paper 85528, University Library of Munich, Germany.
    15. Liu, Xiaochun, 2017. "Can macroeconomic dynamics explain the time variation of risk–return trade-offs in the U.S. financial market?," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 275-293.
    16. Aslanidis, Nektarios & Christiansen, Charlotte & Savva, Christos S., 2016. "Risk-return trade-off for European stock markets," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 84-103.
    17. Jyri Kinnunen & Minna Martikainen, 2017. "Expected Returns and Idiosyncratic Risk: Industry-Level Evidence from Russia," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 53(11), pages 2528-2544, November.
    18. Likai Chen & Ekaterina Smetanina & Wei Biao Wu, 2022. "Estimation of nonstationary nonparametric regression model with multiplicative structure [Income and wealth distribution in macroeconomics: A continuous-time approach]," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 176-214.
    19. Aslanidis, Nektarios & Christiansen, Charlotte & Savva, Christos S., 2020. "Flight-to-safety and the risk-return trade-off: European evidence," Finance Research Letters, Elsevier, vol. 35(C).
    20. Huang, Qiubin & de Haan, Jakob & Scholtens, Bert, 2020. "Does bank capitalization matter for bank stock returns?," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    21. Aragó, V. & Barreda-Tarrazona, I. & Breaban, A. & Matallín, J.C. & Salvador, E., 2022. "Market risk aversion under volatility shifts: An experimental study," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 552-568.
    22. Frazier, David T. & Liu, Xiaochun, 2016. "A new approach to risk-return trade-off dynamics via decomposition," Journal of Economic Dynamics and Control, Elsevier, vol. 62(C), pages 43-55.
    23. Licheng Sun & Liang Meng & Mohammad Najand, 2017. "The Role of U.S. Market on International Risk-Return Tradeoff Relations," The Financial Review, Eastern Finance Association, vol. 52(3), pages 499-526, August.
    24. Jorge M. Uribe, 2018. "“Scaling Down Downside Risk with Inter-Quantile Semivariances”," IREA Working Papers 201826, University of Barcelona, Research Institute of Applied Economics, revised Oct 2018.
    25. Amanjot Singh & Parneet Kaur, 2017. "Does US Financial Stress Explain Risk–Return Dynamics in Indian Equity Market? A Logistic Regression Approach," Vision, , vol. 21(1), pages 13-22, March.
    26. Asgharian, Hossein & Christiansen, Charlotte & Hou, Ai Jun & Wang, Weining, 2021. "Long- and short-run components of factor betas: Implications for stock pricing," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 74(C).
    27. Petar Sabtchevsky & Paul Whelan & Andrea Vedolin & Philippe Mueller, 2017. "Variance Risk Premia on Stocks and Bonds," 2017 Meeting Papers 1161, Society for Economic Dynamics.
    28. Salvador, Enrique & Floros, Christos & Arago, Vicent, 2014. "Re-examining the risk–return relationship in Europe: Linear or non-linear trade-off?," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 60-77.
    29. Liu, Xiaochun, 2017. "Unfolded risk-return trade-offs and links to Macroeconomic Dynamics," Journal of Banking & Finance, Elsevier, vol. 82(C), pages 1-19.
    30. Ahmed, Walid M.A., 2020. "Is there a risk-return trade-off in cryptocurrency markets? The case of Bitcoin," Journal of Economics and Business, Elsevier, vol. 108(C).

  56. Claudia FORONI & Massimiliano MARCELLINO, 2012. "A Comparison of Mixed Frequency Approaches for Modelling Euro Area Macroeconomic Variables," Economics Working Papers ECO2012/07, European University Institute.

    Cited by:

    1. Cláudia Duarte, 2015. "Covariate-augmented unit root tests with mixed-frequency data," Working Papers w201507, Banco de Portugal, Economics and Research Department.
    2. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    3. Foroni, Claudia & Marcellino, Massimiliano & Schumacher, Christian, 2011. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," Discussion Paper Series 1: Economic Studies 2011,35, Deutsche Bundesbank.
    4. Massimiliano Marcellino & Mario Porqueddu & Fabrizio Venditti, 2016. "Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 118-127, January.
    5. Kajal Lahiri & George Monokroussos, 2011. "Nowcasting US GDP: The role of ISM Business Surveys," Discussion Papers 11-01, University at Albany, SUNY, Department of Economics.
    6. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
    7. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    8. Claudia Foroni & Massimiliano Marcellino, 2014. "Mixed frequency structural VARs," Working Paper 2014/01, Norges Bank.
    9. C. Marsilli, 2014. "Variable Selection in Predictive MIDAS Models," Working papers 520, Banque de France.

  57. Òscar Jordà & Malte Knuppel & Massimiliano Marcellino, 2012. "Empirical simultaneous prediction regions for path-forecasts," Working Paper Series 2012-05, Federal Reserve Bank of San Francisco.

    Cited by:

    1. Kung, Ko-Lun & MacMinn, Richard D. & Kuo, Weiyu & Tsai, Chenghsien Jason, 2022. "Multi-population mortality modeling: When the data is too much and not enough," Insurance: Mathematics and Economics, Elsevier, vol. 103(C), pages 41-55.
    2. Svetlana Makarova, 2014. "Risk and Uncertainty: Macroeconomic Perspective," UCL SSEES Economics and Business working paper series 129, UCL School of Slavonic and East European Studies (SSEES).
    3. David F. Hendry & Felix Pretis, 2020. "Analyzing Differences between Scenarios," Economics Papers 2020-W05, Economics Group, Nuffield College, University of Oxford.
    4. Lee, Seohyun, 2017. "Three essays on uncertainty: real and financial effects of uncertainty shocks," MPRA Paper 83617, University Library of Munich, Germany.
    5. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    6. Constantin Burgi, 2016. "What Do We Lose When We Average Expectations?," Working Papers 2016-013, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    7. Knüppel, Malte, 2014. "Forecast-error-based estimation of forecast uncertainty when the horizon is increased," Discussion Papers 40/2014, Deutsche Bundesbank.
    8. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Michael Wolf & Dan Wunderli, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 352-376, May.
    9. Diego Fresoli, 2022. "Bootstrap VAR forecasts: The effect of model uncertainties," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 279-293, March.
    10. Gonçalves Mazzeu, Joao Henrique & Ruiz Ortega, Esther & Veiga, Helena, 2015. "Model uncertainty and the forecast accuracy of ARMA models: A survey," DES - Working Papers. Statistics and Econometrics. WS ws1508, Universidad Carlos III de Madrid. Departamento de Estadística.

  58. Guido Bulligan & Massimiliano Marcellino & Fabrizio Venditti, 2012. "Forecasting economic activity with higher frequency targeted predictors," Temi di discussione (Economic working papers) 847, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Marie Bessec, 2013. "Short‐Term Forecasts of French GDP: A Dynamic Factor Model with Targeted Predictors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(6), pages 500-511, September.
    2. Libero Monteforte & Valentina Raponi, 2019. "Short‐term forecasts of economic activity: Are fortnightly factors useful?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(3), pages 207-221, April.
    3. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    4. an de Meulen, Philipp, 2015. "Das RWI-Kurzfristprognosemodell," RWI Konjunkturberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, vol. 66(2), pages 25-46.
    5. Aaron G. Grech, "undated". "The European Commission’s business and consumer surveys and Maltese macroeconomic trends," CBM Policy Papers PP/05/2019, Central Bank of Malta.
    6. Germán López, 2015. "Forecast Accuracy of Small and Large Scale Dynamic Factor Models in Developing Economies," Working Papers. Serie AD 2015-03, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    7. Ch. Piette & G. Langenus, 2014. "Using BREL to nowcast the Belgian business cycle: the role of survey data," Economic Review, National Bank of Belgium, issue i, pages 75-98, June.
    8. Matteo Luciani & Libero Monteforte, 2012. "Uncertainty and Heterogeneity in factor models forecasting," Working Papers 5, Department of the Treasury, Ministry of the Economy and of Finance.
    9. Roberto Golinelli & Giuseppe Parigi, 2013. "Tracking world trade and GDP in real time," Temi di discussione (Economic working papers) 920, Bank of Italy, Economic Research and International Relations Area.
    10. Valentina Aprigliano & Guerino Ardizzi & Libero Monteforte, 2017. "Using the payment system data to forecast the Italian GDP," Temi di discussione (Economic working papers) 1098, Bank of Italy, Economic Research and International Relations Area.
    11. Christophe Piette, 2016. "Predicting Belgium’s GDP using targeted bridge models," Working Paper Research 290, National Bank of Belgium.
    12. Boriss Siliverstovs, 2017. "Short-term forecasting with mixed-frequency data: a MIDASSO approach," Applied Economics, Taylor & Francis Journals, vol. 49(13), pages 1326-1343, March.
    13. Aaron G. Grech & Reuben Ellul, 2021. "Are the European Commission’s Business and Consumer Survey Results Coincident Indicators for Maltese Economic Activity?," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(1), pages 91-108, April.
    14. Kitlinski, Tobias & an de Meulen, Philipp, 2015. "The role of targeted predictors for nowcasting GDP with bridge models: Application to the Euro area," Ruhr Economic Papers 559, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.

  59. Lorenzo Bencivelli & Massimiliano Marcellino & Gianluca Moretti, 2012. "Selecting predictors by using Bayesian model averaging in bridge models," Temi di discussione (Economic working papers) 872, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    2. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    3. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    4. Guido Bulligan & Fabio Busetti & Michele Caivano & Pietro Cova & Davide Fantino & Alberto Locarno & Lisa Rodano, 2017. "The Bank of Italy econometric model: an update of the main equations and model elasticities," Temi di discussione (Economic working papers) 1130, Bank of Italy, Economic Research and International Relations Area.
    5. David Kohns & Arnab Bhattacharjee, 2019. "Interpreting Big Data in the Macro Economy: A Bayesian Mixed Frequency Estimator," CEERP Working Paper Series 010, Centre for Energy Economics Research and Policy, Heriot-Watt University.
    6. Nuttanan Wichitaksorn, 2020. "Analyzing and Forecasting Thai Macroeconomic Data using Mixed-Frequency Approach," PIER Discussion Papers 146, Puey Ungphakorn Institute for Economic Research.
    7. C. Marsilli, 2014. "Variable Selection in Predictive MIDAS Models," Working papers 520, Banque de France.
    8. Wichitaksorn, Nuttanan, 2022. "Analyzing and forecasting Thai macroeconomic data using mixed-frequency approach," Journal of Asian Economics, Elsevier, vol. 78(C).

  60. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2012. "Common Drifting Volatility in Large Bayesian VARs," CEPR Discussion Papers 8894, C.E.P.R. Discussion Papers.

    Cited by:

    1. Mumtaz, Haroon & Theodoridis, Konstantinos, 2017. "Common and country specific economic uncertainty," Journal of International Economics, Elsevier, vol. 105(C), pages 205-216.
    2. Huber, Florian, 2016. "Density forecasting using Bayesian global vector autoregressions with stochastic volatility," International Journal of Forecasting, Elsevier, vol. 32(3), pages 818-837.
    3. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2020. "Efficient selection of hyperparameters in large Bayesian VARs using automatic differentiation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 934-943, September.
    4. Joshua C. C. Chan & Gary Koop & Xuewen Yu, 2024. "Large Order-Invariant Bayesian VARs with Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(2), pages 825-837, April.
    5. Joshua C. C. Chan & Xuewen Yu, 2022. "Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility," Papers 2206.08438, arXiv.org.
    6. Gefang, Deborah & Koop, Gary & Poon, Aubrey, 2023. "Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage," International Journal of Forecasting, Elsevier, vol. 39(1), pages 346-363.
    7. Uribe Jorge M. & Chuliá Helena, 2023. "Expected, unexpected, good and bad aggregate uncertainty," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 27(2), pages 265-284, April.
    8. Jan Philipp Fritsche & Mathias Klein & Malte Rieth, 2020. "Government Spending Multipliers in (Un)certain Times," Discussion Papers of DIW Berlin 1901, DIW Berlin, German Institute for Economic Research.
    9. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2023. "Large Time‐Varying Volatility Models for Hourly Electricity Prices," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 545-573, June.
    10. Markus Heinrich & Magnus Reif, 2018. "Forecasting using mixed-frequency VARs with time-varying parameters," ifo Working Paper Series 273, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    11. Scott Brave & R. Andrew Butters & Alejandro Justiniano, 2016. "Forecasting Economic Activity with Mixed Frequency Bayesian VARs," Working Paper Series WP-2016-5, Federal Reserve Bank of Chicago.
    12. Deborah Gefang & Gary Koop & Aubrey Poon, 2019. "Variational Bayesian inference in large Vector Autoregressions with hierarchical shrinkage," CAMA Working Papers 2019-08, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    13. Bańbura, Marta & Giannone, Domenico & Lenza, Michele, 2015. "Conditional forecasts and scenario analysis with vector autoregressions for large cross-sections," International Journal of Forecasting, Elsevier, vol. 31(3), pages 739-756.
    14. Kyle Jurado & Sydney C. Ludvigson & Serena Ng, 2015. "Measuring Uncertainty," American Economic Review, American Economic Association, vol. 105(3), pages 1177-1216, March.
    15. Cross, Jamie L. & Hou, Chenghan & Koop, Gary & Poon, Aubrey, 2023. "Large stochastic volatility in mean VARs," Journal of Econometrics, Elsevier, vol. 236(1).
    16. Piergiorgio Alessandri & Haroon Mumtaz, 2014. "Financial regimes and uncertainty shocks," BCAM Working Papers 1404, Birkbeck Centre for Applied Macroeconomics.
    17. Tsionas, Mike G. & Izzeldin, Marwan & Trapani, Lorenzo, 2022. "Estimation of large dimensional time varying VARs using copulas," European Economic Review, Elsevier, vol. 141(C).
    18. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    19. Chiu, Ching-Wai (Jeremy) & Mumtaz, Haroon & Pinter, Gabor, 2016. "VAR models with non-Gaussian shocks," LSE Research Online Documents on Economics 86238, London School of Economics and Political Science, LSE Library.
    20. Simon Beyeler, 2019. "Streamlining Time-varying VAR with a Factor Structure in the Parameters," Working Papers 19.03, Swiss National Bank, Study Center Gerzensee.
    21. Brave, Scott A. & Butters, R. Andrew & Justiniano, Alejandro, 2019. "Forecasting economic activity with mixed frequency BVARs," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1692-1707.
    22. Gary Koop & Dimitris Korobilis & Davide Pettenuzzo, 2016. "Bayesian Compressed Vector Autoregressions," Working Papers 103, Brandeis University, Department of Economics and International Business School.
    23. Gary Koop & Dimitris Korobilis, 2012. "Large Time-Varying Parameter VARs," Working Paper series 11_12, Rimini Centre for Economic Analysis.
    24. Chan, Joshua C.C. & Eisenstat, Eric & Strachan, Rodney W., 2020. "Reducing the state space dimension in a large TVP-VAR," Journal of Econometrics, Elsevier, vol. 218(1), pages 105-118.
    25. Knüppel, Malte & Krüger, Fabian, 2017. "Forecast Uncertainty, Disagreement, and Linear Pools of Density Forecasts," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168294, Verein für Socialpolitik / German Economic Association.
    26. Follett, Lendie & Yu, Cindy, 2019. "Achieving parsimony in Bayesian vector autoregressions with the horseshoe prior," Econometrics and Statistics, Elsevier, vol. 11(C), pages 130-144.
    27. Joshua C.C. Chan & Eric Eisenstat & Chenghan Hou & Gary Koop, 2018. "Composite likelihood methods for large Bayesian VARs with stochastic volatility," CAMA Working Papers 2018-26, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    28. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Density forecasts of inflation: a quantile regression forest approach," Working Paper Series 2830, European Central Bank.
    29. Huber, Florian & Krisztin, Tamás & Piribauer, Philipp, 2014. "Forecasting Global Equity Indices Using Large Bayesian VARs," Department of Economics Working Paper Series 184, WU Vienna University of Economics and Business.
    30. Francisco Serranito & Nicolas Himounet & Julien Vauday, 2023. "Uncertainty is bad for Business. Really?," Working Papers hal-04219283, HAL.
    31. Matteo Iacopini & Francesco Ravazzolo & Luca Rossini, 2022. "Bayesian Multivariate Quantile Regression with alternative Time-varying Volatility Specifications," Papers 2211.16121, arXiv.org.
    32. Todd E. Clark & Michael W. McCracken & Elmar Mertens, 2020. "Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors," The Review of Economics and Statistics, MIT Press, vol. 102(1), pages 17-33, March.
    33. Joshua C. C. Chan, 2022. "Comparing Stochastic Volatility Specifications for Large Bayesian VARs," Papers 2208.13255, arXiv.org.
    34. Dovern, Jonas & Feldkircher, Martin & Huber, Florian, 2015. "Does Joint Modelling of the World Economy Pay Off? Evaluating Multivariate Forecasts from a Bayesian GVAR," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112999, Verein für Socialpolitik / German Economic Association.
    35. Gregor Kastner, 2016. "Sparse Bayesian time-varying covariance estimation in many dimensions," Papers 1608.08468, arXiv.org, revised Nov 2017.
    36. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2022. "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," CEPR Discussion Papers 17512, C.E.P.R. Discussion Papers.
    37. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2014. "No Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates," CEPR Discussion Papers 9848, C.E.P.R. Discussion Papers.
    38. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Large Vector Autoregressions with Asymmetric Priors," Working Papers 759, Queen Mary University of London, School of Economics and Finance.
    39. Martin Feldkircher & Florian Huber & Michael Pfarrhofer, 2020. "Measuring the Effectiveness of US Monetary Policy during the COVID-19 Recession," Papers 2007.15419, arXiv.org.
    40. Florian Huber, 2014. "Density Forecasting using Bayesian Global Vector Autoregressions with Common Stochastic Volatility," Department of Economics Working Papers wuwp179, Vienna University of Economics and Business, Department of Economics.
    41. Lenza, Michele & Cimadomo, Jacopo & Giannone, Domenico & Monti, Francesca & Sokol, Andrej, 2021. "Nowcasting with Large Bayesian Vector Autoregressions," CEPR Discussion Papers 15854, C.E.P.R. Discussion Papers.
    42. Chenghan Hou & Bao Nguyen & Bo Zhang, 2023. "Real‐time forecasting of the Australian macroeconomy using flexible Bayesian VARs," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 418-451, March.
    43. Haroon Mumtaz & Laura Sunder-Plassmann & Angeliki Theophilopoulou, 2016. "The State Level Impact of Uncertainty Shocks," Working Papers 793, Queen Mary University of London, School of Economics and Finance.
    44. Bognanni, Mark & Zito, John, 2020. "Sequential Bayesian inference for vector autoregressions with stochastic volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    45. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2020. "Large Time-Varying Volatility Models for Electricity Prices," Working Papers No 05/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    46. Florian Huber & Gary Koop, 2023. "Fast and Order-invariant Inference in Bayesian VARs with Non-Parametric Shocks," Papers 2305.16827, arXiv.org.
    47. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    48. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    49. Arias, Jonas E. & Rubio-Ramírez, Juan F. & Shin, Minchul, 2023. "Macroeconomic forecasting and variable ordering in multivariate stochastic volatility models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1054-1086.
    50. Nicolas Himounet, 2021. "Searching for the Nature of Uncertainty: Macroeconomic VS Financial," Working Papers 2021.05, International Network for Economic Research - INFER.
    51. Primiceri, Giorgio & Lenza, Michele, 2020. "How to Estimate a VAR after March 2020," CEPR Discussion Papers 15245, C.E.P.R. Discussion Papers.
    52. Eric Eisenstat & Joshua C.C. Chan & Rodney W. Strachan, 2018. "Reducing Dimensions in a Large TVP-VAR," Working Paper Series 43, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    53. Ankargren Sebastian & Unosson Måns & Yang Yukai, 2020. "A Flexible Mixed-Frequency Vector Autoregression with a Steady-State Prior," Journal of Time Series Econometrics, De Gruyter, vol. 12(2), pages 1-41, July.
    54. Oguzhan Cepni & I. Ethem Guney & Norman R. Swanson, 2020. "Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 18-36, January.
    55. Jan Pruser & Florian Huber, 2023. "Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions," Papers 2301.13604, arXiv.org, revised Sep 2023.
    56. Byrne, Joseph P. & Cao, Shuo & Korobilis, Dimitris, 2019. "Decomposing global yield curve co-movement," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 500-513.
    57. Ellington, Michael, 2022. "Fat tails, serial dependence, and implied volatility index connections," European Journal of Operational Research, Elsevier, vol. 299(2), pages 768-779.
    58. Samuel F. Onipede & Nafiu A. Bashir & Jamaladeen Abubakar, 2023. "Small open economies and external shocks: an application of Bayesian global vector autoregression model," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 1673-1699, April.
    59. Martin Feldkircher & Nico Hauzenberger, 2019. "How useful are time-varying parameter models for forecasting economic growth in CESEE?," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q1/19, pages 29-48.
    60. Joshua C. C. Chan, 2019. "Large Bayesian vector autoregressions," CAMA Working Papers 2019-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    61. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhuang, Xin-Tian, 2019. "Non-Gaussian VARMA model with stochastic volatility and applications in stock market bubbles," Chaos, Solitons & Fractals, Elsevier, vol. 121(C), pages 129-136.
    62. Marcellino, Massimiliano & Clark, Todd & Huber, Florian & Koop, Gary & Pfarrhofer, Michael, 2022. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," CEPR Discussion Papers 17461, C.E.P.R. Discussion Papers.
    63. Christine Garnier & Elmar Mertens & Edward Nelson, 2015. "Trend Inflation in Advanced Economies," International Journal of Central Banking, International Journal of Central Banking, vol. 11(4), pages 65-136, September.
    64. Gregor Kastner & Florian Huber, 2020. "Sparse Bayesian vector autoregressions in huge dimensions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1142-1165, November.
    65. Haroon Mumtaz, 2020. "A Generalised Stochastic Volatility in Mean VAR. An Updated Algorithm," Working Papers 908, Queen Mary University of London, School of Economics and Finance.
    66. Thore Schlaak & Malte Rieth & Maximilian Podstawski, 2023. "Monetary policy, external instruments, and heteroskedasticity," Quantitative Economics, Econometric Society, vol. 14(1), pages 161-200, January.
    67. Reifschneider, David & Tulip, Peter, 2019. "Gauging the uncertainty of the economic outlook using historical forecasting errors: The Federal Reserve’s approach," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1564-1582.
    68. Berg, Tim Oliver & Henzel, Steffen, 2013. "Point and Density Forecasts for the Euro Area Using Many Predictors: Are Large BVARs Really Superior?," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79783, Verein für Socialpolitik / German Economic Association.
    69. Michele Lenza & Giorgio E. Primiceri, 2022. "How to estimate a vector autoregression after March 2020," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 688-699, June.
    70. Irina Zviadadze, 2017. "Term Structure of Consumption Risk Premia in the Cross Section of Currency Returns," Journal of Finance, American Finance Association, vol. 72(4), pages 1529-1566, August.
    71. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    72. Joshua C. C. Chan, 2019. "Minnesota-type adaptive hierarchical priors for large Bayesian VARs," CAMA Working Papers 2019-61, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    73. Jamie L. Cross & Chenghan Hou & Aubrey Poon, 2018. "International Transmission of Macroeconomic Uncertainty in Small Open Economies: An Empirical Approach," Working Papers No 12/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    74. Mark Bognanni & John Zito, 2019. "Sequential Bayesian Inference for Vector Autoregressions with Stochastic Volatility," Working Papers 19-29, Federal Reserve Bank of Cleveland.
    75. Danilo Cascaldi-Garcia, 2022. "Pandemic Priors," International Finance Discussion Papers 1352, Board of Governors of the Federal Reserve System (U.S.).
    76. Nam, Kyungsik, 2021. "Investigating the effect of climate uncertainty on global commodity markets," Energy Economics, Elsevier, vol. 96(C).
    77. Ankargren, Sebastian & Jonéus, Paulina, 2021. "Simulation smoothing for nowcasting with large mixed-frequency VARs," Econometrics and Statistics, Elsevier, vol. 19(C), pages 97-113.
    78. Prüser, Jan, 2023. "Data-based priors for vector error correction models," International Journal of Forecasting, Elsevier, vol. 39(1), pages 209-227.
    79. Todd E. Clark & Francesco Ravazzolo, 2012. "The macroeconomic forecasting performance of autoregressive models with alternative specifications of time-varying volatility," Working Paper 2012/09, Norges Bank.
    80. Crespo Cuaresma, Jesús & Huber, Florian & Onorante, Luca, 2020. "Fragility and the effect of international uncertainty shocks," Journal of International Money and Finance, Elsevier, vol. 108(C).
    81. Jamie L. Cross & Chenghan Hou & Gary Koop, 2021. "Macroeconomic Forecasting with Large Stochastic Volatility in Mean VARs," Working Papers No 04/2021, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    82. Barbara Rossi, 2019. "Identifying and Estimating the Effects of Unconventional Monetary Policy in the Data: How to Do It and What Have We Learned?," Working Papers 1081, Barcelona School of Economics.
    83. Fu, Bowen, 2023. "Measuring the trend real interest rate in a data-rich environment," Journal of Economic Dynamics and Control, Elsevier, vol. 147(C).
    84. Stefan Griller & Florian Huber & Michael Pfarrhofer, 2022. "Measuring Shocks to Central Bank Independence using Legal Rulings," Papers 2202.12695, arXiv.org.
    85. Thomas B Götz & Klemens Hauzenberger, 2021. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure," The Econometrics Journal, Royal Economic Society, vol. 24(3), pages 442-461.
    86. Mike Tsionas & Marwan Izzeldin & Lorenzo Trapani, 2019. "Bayesian estimation of large dimensional time varying VARs using copulas," Papers 1912.12527, arXiv.org.
    87. Jonas Dovern & Martin Feldkircher & Florian Huber, 2015. "Does Joint Modelling of the World Economy Pay Off? Evaluating Global Forecasts from a Bayesian GVAR," Working Papers 200, Oesterreichische Nationalbank (Austrian Central Bank).
    88. Valentina Aprigliano & Alessandro Borin & Francesco Paolo Conteduca & Simone Emiliozzi & Marco Flaccadoro & Sabina Marchetti & Stefania Villa, 2021. "Forecasting Italian GDP growth with epidemiological data," Questioni di Economia e Finanza (Occasional Papers) 664, Bank of Italy, Economic Research and International Relations Area.
    89. MeiChi Huang, 2022. "Time‐varying impacts of expectations on housing markets across hot and cold phases," International Finance, Wiley Blackwell, vol. 25(2), pages 249-265, August.
    90. Gary Koop & Stuart McIntyre & James Mitchell, 2018. "UK Regional Nowcasting using a Mixed Frequency Vector Autoregressive Model," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-07, Economic Statistics Centre of Excellence (ESCoE).
    91. Hardik A. Marfatia & Qiang Ji & Jiawen Luo, 2022. "Forecasting the volatility of agricultural commodity futures: The role of co‐volatility and oil volatility," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 383-404, March.
    92. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2016. "A MIDAS approach to modeling first and second moment dynamics," Journal of Econometrics, Elsevier, vol. 193(2), pages 315-334.
    93. Valeriu Nalban & Andra Smadu, 2020. "Financial disruptions and heightened uncertainty: a case for timely policy action," Working Papers 687, DNB.
    94. Nalban, Valeriu & Smădu, Andra, 2021. "Asymmetric effects of uncertainty shocks: Normal times and financial disruptions are different," Journal of Macroeconomics, Elsevier, vol. 69(C).
    95. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    96. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano, 2021. "Using time-varying volatility for identification in Vector Autoregressions: An application to endogenous uncertainty," Journal of Econometrics, Elsevier, vol. 225(1), pages 47-73.
    97. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Large Vector Autoregressions with Stochastic Volatility and Flexible Priors," Working Papers (Old Series) 1617, Federal Reserve Bank of Cleveland.
    98. Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022. "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers 2212.03471, arXiv.org, revised Jul 2023.
    99. Carriero, Andrea & Mumtaz, Haroon & Theophilopoulou, Angeliki, 2015. "Macroeconomic information, structural change, and the prediction of fiscal aggregates," International Journal of Forecasting, Elsevier, vol. 31(2), pages 325-348.
    100. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    101. Jamie L. Cross & Aubrey Poon, 2020. "On the contribution of international shocks in Australian business cycle fluctuations," Empirical Economics, Springer, vol. 59(6), pages 2613-2637, December.
    102. Joshua C.C. Chan & Rodney W. Strachan, 2023. "Bayesian State Space Models In Macroeconometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 58-75, February.
    103. Joshua C. C. Chan, 2022. "Asymmetric conjugate priors for large Bayesian VARs," Quantitative Economics, Econometric Society, vol. 13(3), pages 1145-1169, July.
    104. Paul Labonne, 2022. "Asymmetric Uncertainty: Nowcasting Using Skewness in Real-time Data," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-23, Economic Statistics Centre of Excellence (ESCoE).
    105. Chuliá, Helena & Guillén, Montserrat & Uribe, Jorge M., 2017. "Measuring uncertainty in the stock market," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 18-33.
    106. Zhang, Bo & Nguyen, Bao H., 2020. "Real-time forecasting of the Australian macroeconomy using Bayesian VARs," Working Papers 2020-12, University of Tasmania, Tasmanian School of Business and Economics.
    107. Heinrich, Markus, 2020. "Does the Current State of the Business Cycle matter for Real-Time Forecasting? A Mixed-Frequency Threshold VAR approach," EconStor Preprints 219312, ZBW - Leibniz Information Centre for Economics.
    108. Chan, Joshua C.C. & Poon, Aubrey & Zhu, Dan, 2023. "High-dimensional conditionally Gaussian state space models with missing data," Journal of Econometrics, Elsevier, vol. 236(1).
    109. Dimitrios P. Louzis, 2019. "Steady‐state modeling and macroeconomic forecasting quality," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 285-314, March.
    110. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano, 2019. "Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors," Journal of Econometrics, Elsevier, vol. 212(1), pages 137-154.
    111. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2020. "The economic drivers of volatility and uncertainty," Temi di discussione (Economic working papers) 1285, Bank of Italy, Economic Research and International Relations Area.
    112. Prüser, Jan & Blagov, Boris, 2022. "Improving inference and forecasting in VAR models using cross-sectional information," Ruhr Economic Papers 960, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    113. Jiawen Luo & Langnan Chen, 2019. "Multivariate realized volatility forecasts of agricultural commodity futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(12), pages 1565-1586, December.
    114. Antonio Pacifico, 2022. "Structural Compressed Panel VAR with Stochastic Volatility: A Robust Bayesian Model Averaging Procedure," Econometrics, MDPI, vol. 10(3), pages 1-24, July.
    115. Sebastian Ankargren & Paulina Jon'eus, 2019. "Estimating Large Mixed-Frequency Bayesian VAR Models," Papers 1912.02231, arXiv.org.
    116. Luo, Jiawen & Marfatia, Hardik A. & Ji, Qiang & Klein, Tony, 2023. "Co-volatility and asymmetric transmission of risks between the global oil and China's futures markets," Energy Economics, Elsevier, vol. 117(C).
    117. Cross, Jamie L. & Hou, Chenghan & Poon, Aubrey, 2020. "Macroeconomic forecasting with large Bayesian VARs: Global-local priors and the illusion of sparsity," International Journal of Forecasting, Elsevier, vol. 36(3), pages 899-915.
    118. Haroon Mumtaz & Konstantinos Theodoridis, 2014. "The Changing Transmission of Uncertainty shocks in the US: An Empirical Analysis," Working Papers 735, Queen Mary University of London, School of Economics and Finance.
    119. Hadjiantoni, Stella & Kontoghiorghes, Erricos John, 2022. "An alternative numerical method for estimating large-scale time-varying parameter seemingly unrelated regressions models," Econometrics and Statistics, Elsevier, vol. 21(C), pages 1-18.
    120. Gary Koop & Stuart McIntyre & James Mitchell, 2020. "UK regional nowcasting using a mixed frequency vector auto‐regressive model with entropic tilting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 91-119, January.
    121. Tim Oliver Berg & Steffen Henzel, 2014. "Point and Density Forecasts for the Euro Area Using Bayesian VARs," CESifo Working Paper Series 4711, CESifo.
    122. David L. Reifschneider & Peter Tulip, 2017. "Gauging the Uncertainty of the Economic Outlook Using Historical Forecasting Errors : The Federal Reserve's Approach," Finance and Economics Discussion Series 2017-020, Board of Governors of the Federal Reserve System (U.S.).
    123. Nguyen, BH & Zhang, Bo, 2022. "Forecasting oil Prices: can large BVARs help?," Working Papers 2022-04, University of Tasmania, Tasmanian School of Business and Economics.
    124. Helena Chuliá & Jorge M. Uribe, 2019. "“Expected, Unexpected, Good and Bad Uncertainty"," IREA Working Papers 201919, University of Barcelona, Research Institute of Applied Economics, revised Nov 2019.
    125. Malte Knüppel & Fabian Krüger, 2022. "Forecast uncertainty, disagreement, and the linear pool," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 23-41, January.
    126. Aubrey Poon, 2018. "Assessing the Synchronicity and Nature of Australian State Business Cycles," The Economic Record, The Economic Society of Australia, vol. 94(307), pages 372-390, December.
    127. Paul Labonne, 2020. "Capturing GDP nowcast uncertainty in real time," Papers 2012.02601, arXiv.org, revised Oct 2021.
    128. Hartwig, Benny, 2022. "Bayesian VARs and prior calibration in times of COVID-19," Discussion Papers 52/2022, Deutsche Bundesbank.
    129. Michael Pfarrhofer, 2020. "Forecasts with Bayesian vector autoregressions under real time conditions," Papers 2004.04984, arXiv.org.
    130. James P. LeSage & Daniel Hendrikz, 2019. "Large Bayesian vector autoregressive forecasting for regions: A comparison of methods based on alternative disturbance structures," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 62(3), pages 563-599, June.

  61. Szabolcs Deák & Lionel Fontagné & Marco Maffezzoli & Massimiliano Marcellino, 2012. "The banking and distribution sectors in a small open economy DSGE Model," Working Papers 454, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Yuliya Rychalovska & Massimiliano Marcellino (EUI), 2013. "An estimated DSGE model of a Small Open Economy within the Monetary Union: Forecasting and Structural Analysis," EcoMod2013 5302, EcoMod.
    2. Massimiliano Marcellino & Yuliya Rychalovska, 2014. "Forecasting with a DSGE Model of a Small Open Economy within the Monetary Union," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(5), pages 315-338, August.

  62. Ferrara, L. & Marcellino, M. & Mogliani, M., 2012. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," Working papers 383, Banque de France.

    Cited by:

    1. Knut Lehre Seip & Yunus Yilmaz & Michael Schröder, 2019. "Comparing Sentiment- and Behavioral-Based Leading Indexes for Industrial Production: When Does Each Fail?," Economies, MDPI, vol. 7(4), pages 1-18, October.
    2. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
    3. Caterina Forti Grazzini & Massimo Guidolin, 2013. "Forecasting yield spreads under crisis-induced multiple breakpoints," Applied Economics Letters, Taylor & Francis Journals, vol. 20(18), pages 1656-1664, December.
    4. Amélie Charles & Olivier Darné & Laurent Ferrara, 2014. "Does the Great Recession imply the end of the Great Moderation? International evidence," Working Papers hal-04141344, HAL.
    5. Ana B. Galvão & Michael T. Owyang, 2014. "Financial stress regimes and the macroeconomy," Working Papers 2014-20, Federal Reserve Bank of St. Louis.
    6. Marcellino, Massimiliano & Kapetanios, George & Dendramis, Yiannis, 2020. "A Similarity-based Approach for Macroeconomic Forecasting," CEPR Discussion Papers 14469, C.E.P.R. Discussion Papers.
    7. Kurmaş Akdoğan, 2017. "Unemployment hysteresis and structural change in Europe," Empirical Economics, Springer, vol. 53(4), pages 1415-1440, December.
    8. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Density forecasts of inflation: a quantile regression forest approach," Working Paper Series 2830, European Central Bank.
    9. Zanetti Chini, Emilio, 2018. "Forecasting dynamically asymmetric fluctuations of the U.S. business cycle," International Journal of Forecasting, Elsevier, vol. 34(4), pages 711-732.
    10. Boris Blagov & Michael Funke & Richhild Moessner, 2015. "Modelling the time-variation in euro area lending spreads," BIS Working Papers 526, Bank for International Settlements.
    11. Bartkus Algirdas, 2016. "A New Model with Regime Switching Errors: Forecasting Gdp in Times of Great Recession," Ekonomika (Economics), Sciendo, vol. 95(2), pages 7-29, February.
    12. Andrea Carriero & Galvao, Ana Beatriz & Kapetanios, George, 2016. "A comprehensive evaluation of macroeconomic forecasting methods," EMF Research Papers 10, Economic Modelling and Forecasting Group.
    13. Zeyyad Mandalinci, 2015. "Forecasting Inflation in Emerging Markets: An Evaluation of Alternative Models," CReMFi Discussion Papers 3, CReMFi, School of Economics and Finance, QMUL.
    14. Paul Ho, 2021. "Forecasting in the Absence of Precedent," Working Paper 21-10, Federal Reserve Bank of Richmond.
    15. Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2012. "Forecasting UK GDP growth, inflation and interest rates under structural change: a comparison of models with time-varying parameters," Bank of England working papers 450, Bank of England.
    16. Federico Lampis, 2016. "Forecasting the sectoral GVA of a small Spanish region," Economics and Business Letters, Oviedo University Press, vol. 5(2), pages 38-44.
    17. Kevin Moran & Simplice Aimé Nono & Imad Rherrad, 2018. "Forecasting with Many Predictors: How Useful are National and International Confidence Data?," Cahiers de recherche 1814, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    18. Schlösser, Alexander, 2020. "Forecasting industrial production in Germany: The predictive power of leading indicators," Ruhr Economic Papers 838, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    19. Mahmut Gunay, 2016. "Forecasting Turkish GDP Growth with Financial Variables and Confidence Indicators," CBT Research Notes in Economics 1614, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    20. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
    21. Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers 15114, C.E.P.R. Discussion Papers.
    22. Di Caro, Paolo, 2014. "Regional recessions and recoveries in theory and practice: a resilience-based overview," MPRA Paper 60300, University Library of Munich, Germany.
    23. Heinrich, Markus, 2020. "Does the Current State of the Business Cycle matter for Real-Time Forecasting? A Mixed-Frequency Threshold VAR approach," EconStor Preprints 219312, ZBW - Leibniz Information Centre for Economics.
    24. Kurmaş Akdoğan, 2015. "Unemployment Hysteresis and Structural Change in Europe," EY International Congress on Economics II (EYC2015), November 5-6, 2015, Ankara, Turkey 266, Ekonomik Yaklasim Association.
    25. Rafael Ravnik, 2014. "Short-Term Forecasting of GDP under Structural Changes," Working Papers 40, The Croatian National Bank, Croatia.
    26. Pablo Guerróon‐Quintana & Molin Zhong, 2023. "Macroeconomic forecasting in times of crises," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 295-320, April.
    27. Jörg Döpke & Ulrich Fritsche & Christian Pierdzioch, 2015. "Predicting Recessions With Boosted Regression Trees," Working Papers 2015-004, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    28. Kajal Lahiri & Liu Yang, 2023. "Predicting binary outcomes based on the pair-copula construction," Empirical Economics, Springer, vol. 64(6), pages 3089-3119, June.
    29. Kurmaş Akdoğan, 2015. "Asymmetric Behaviour of Inflation around the Target in Inflation-Targeting Countries," Scottish Journal of Political Economy, Scottish Economic Society, vol. 62(5), pages 486-504, November.
    30. Benjamin Garcia & Arsenios Skaperdas, 2017. "Inferring the Shadow Rate from Real Activity," Finance and Economics Discussion Series 2017-106, Board of Governors of the Federal Reserve System (U.S.).

  63. Schumacher, Christian & Marcellino, Massimiliano & Foroni, Claudia, 2012. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," CEPR Discussion Papers 8828, C.E.P.R. Discussion Papers.

    Cited by:

    1. Winkelried, Diego, 2012. "Predicting quarterly aggregates with monthly indicators," Working Papers 2012-023, Banco Central de Reserva del Perú.
    2. Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 366-380, July.
    3. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    4. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    5. Götz, T.B. & Hecq, A.W., 2013. "Nowcasting causality in mixed frequency vector autoregressive models," Research Memorandum 050, Maastricht University, Graduate School of Business and Economics (GSBE).
    6. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2014. "Combining distributions of real-time forecasts: An application to U.S. growth," Research Memorandum 027, Maastricht University, Graduate School of Business and Economics (GSBE).
    7. Marie Bessec & Othman Bouabdallah, 2015. "Forecasting GDP over the Business Cycle in a Multi-Frequency and Data-Rich Environment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(3), pages 360-384, June.
    8. Hecq, A.W. & Götz, T.B. & Urbain, J.R.Y.J., 2012. "Forecasting Mixed Frequency Time Series with ECM-MIDAS Models," Research Memorandum 012, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    9. Salisu, Afees A. & Ogbonna, Ahamuefula E., 2019. "Another look at the energy-growth nexus: New insights from MIDAS regressions," Energy, Elsevier, vol. 174(C), pages 69-84.
    10. Gani Ramadani & Magdalena Petrovska & Vesna Bucevska, 2021. "Evaluation of mixed frequency approaches for tracking near-term economic developments in North Macedonia," Working Papers 2021-03, National Bank of the Republic of North Macedonia.
    11. Santiago Etchegaray Alvarez, 2022. "Proyecciones macroeconómicas con datos en frecuencias mixtas. Modelos ADL-MIDAS, U-MIDAS y TF-MIDAS con aplicaciones para Uruguay," Documentos de trabajo 2022004, Banco Central del Uruguay.
    12. Maxime Leboeuf & Louis Morel, 2014. "Forecasting Short-Term Real GDP Growth in the Euro Area and Japan Using Unrestricted MIDAS Regressions," Discussion Papers 14-3, Bank of Canada.
    13. Elena Andreou & Andros Kourtellos, 2015. "The State and the Future of Cyprus Macroeconomic Forecasting," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 9(1), pages 73-90, June.
    14. Fady Barsoum & Sandra Stankiewicz, 2013. "Forecasting GDP Growth Using Mixed-Frequency Models With Switching Regimes," Working Paper Series of the Department of Economics, University of Konstanz 2013-10, Department of Economics, University of Konstanz.
    15. Ramazan Yanik & Asfia Binte Osman & Ozcan Ozturk, 2020. "Impact of manufacturing PMI on stock market index: A study on Turkey," Journal of Administrative and Business Studies, Professor Dr. Usman Raja, vol. 6(3), pages 104-108.
    16. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2015. "Testing for Granger causality in large mixed-frequency VARs," Discussion Papers 45/2015, Deutsche Bundesbank.
    17. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2014. "Density forecasts with MIDAS models," Working Paper 2014/10, Norges Bank.
    18. Ramadani Gani & Petrovska Magdalena & Bucevska Vesna, 2021. "Evaluation of Mixed Frequency Approaches for Tracking Near-Term Economic Developments in North Macedonia," South East European Journal of Economics and Business, Sciendo, vol. 16(2), pages 43-52, December.
    19. Michal Franta & David Havrlant & Marek Rusnak, 2014. "Forecasting Czech GDP Using Mixed-Frequency Data Models," Working Papers 2014/08, Czech National Bank.
    20. Valentina Aprigliano & Alessandro Borin & Francesco Paolo Conteduca & Simone Emiliozzi & Marco Flaccadoro & Sabina Marchetti & Stefania Villa, 2021. "Forecasting Italian GDP growth with epidemiological data," Questioni di Economia e Finanza (Occasional Papers) 664, Bank of Italy, Economic Research and International Relations Area.
    21. Davide Pettenuzzo & Rossen Valkanov & Allan Timmermann, 2014. "A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics," Working Papers 76, Brandeis University, Department of Economics and International Business School.
    22. Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2015. "Do high-frequency financial data help forecast oil prices? The MIDAS touch at work," International Journal of Forecasting, Elsevier, vol. 31(2), pages 238-252.
    23. Warmedinger, Thomas & Paredes, Joan & Asimakopoulos, Stylianos, 2013. "Forecasting fiscal time series using mixed frequency data," Working Paper Series 1550, European Central Bank.
    24. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2013. "Testing for Granger Causality with Mixed Frequency Data," CEPR Discussion Papers 9655, C.E.P.R. Discussion Papers.
    25. Bonino-Gayoso, Nicolás & García-Hiernaux, Alfredo, 2019. "TF-MIDAS: a new mixed-frequency model to forecast macroeconomic variables," MPRA Paper 93366, University Library of Munich, Germany.
    26. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
    27. Freitag L., 2014. "Default probabilities, CDS premiums and downgrades : A probit-MIDAS analysis," Research Memorandum 038, Maastricht University, Graduate School of Business and Economics (GSBE).
    28. Schumacher, Christian, 2014. "MIDAS regressions with time-varying parameters: An application to corporate bond spreads and GDP in the Euro area," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100289, Verein für Socialpolitik / German Economic Association.
    29. Semih Emre Çekin & Victor J. Valcarcel, 2020. "Inflation volatility and inflation in the wake of the great recession," Empirical Economics, Springer, vol. 59(4), pages 1997-2015, October.
    30. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    31. Francisco Blasques & Siem Jan Koopman & Max Mallee, 2014. "Low Frequency and Weighted Likelihood Solutions for Mixed Frequency Dynamic Factor Models," Tinbergen Institute Discussion Papers 14-105/III, Tinbergen Institute.
    32. Claudia Foroni & Massimiliano Marcellino, 2014. "Mixed frequency structural VARs," Working Paper 2014/01, Norges Bank.
    33. Conefrey, Thomas & Walsh, Graeme, 2018. "A Monthly Indicator of Economic Activity for Ireland," Economic Letters 14/EL/18, Central Bank of Ireland.
    34. Heiner Mikosch & Ying Zhang, 2014. "Forecasting Chinese GDP Growth with Mixed Frequency Data," KOF Working papers 14-359, KOF Swiss Economic Institute, ETH Zurich.
    35. Dirk Drechsel & Stefan Neuwirth, 2016. "Taming volatile high frequency data with long lag structure: An optimal filtering approach for forecasting," KOF Working papers 16-407, KOF Swiss Economic Institute, ETH Zurich.
    36. C. Marsilli, 2014. "Variable Selection in Predictive MIDAS Models," Working papers 520, Banque de France.
    37. Trujillo-Barrera, Andres & Pennings, Joost M.E., 2013. "Energy and Food Commodity Prices Linkage: An Examination with Mixed-Frequency Data," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150465, Agricultural and Applied Economics Association.

  64. Marcellino, Massimiliano & Eickmeier, Sandra & Lemke, Wolfgang, 2011. "Classical time-varying FAVAR models - Estimation, forecasting and structural analysis," CEPR Discussion Papers 8321, C.E.P.R. Discussion Papers.

    Cited by:

    1. Han, Xu & Inoue, Atsushi, 2015. "Tests For Parameter Instability In Dynamic Factor Models," Econometric Theory, Cambridge University Press, vol. 31(5), pages 1117-1152, October.
    2. Mawuli Segnon & Rangan Gupta & Stelios Bekiros & Mark E. Wohar, 2018. "Forecasting US GNP growth: The role of uncertainty," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(5), pages 541-559, August.
    3. Eickmeier, Sandra & Ng, Tim, 2011. "How Do Credit Supply Shocks Propagate Internationally? A GVAR approach," CEPR Discussion Papers 8720, C.E.P.R. Discussion Papers.
    4. Prieto, Esteban & Eickmeier, Sandra & Marcellino, Massimiliano, 2013. "Time variation in macro-financial linkages," Discussion Papers 13/2013, Deutsche Bundesbank.
    5. Han, Xu, 2015. "Tests for overidentifying restrictions in Factor-Augmented VAR models," Journal of Econometrics, Elsevier, vol. 184(2), pages 394-419.
    6. Bates, Brandon J. & Plagborg-Møller, Mikkel & Stock, James H. & Watson, Mark W., 2013. "Consistent Factor Estimation in Dynamic Factor Models with Structural Instability," Scholarly Articles 28469786, Harvard University Department of Economics.
    7. Pappa, Evi & Molteni, Francesco, 2017. "The Combination of Monetary and Fiscal Policy Shocks: A TVP-FAVAR Approach," CEPR Discussion Papers 12541, C.E.P.R. Discussion Papers.
    8. Breitung, Jörg & Eickmeier, Sandra, 2011. "Testing for structural breaks in dynamic factor models," Journal of Econometrics, Elsevier, vol. 163(1), pages 71-84, July.
    9. Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2012. "Forecasting UK GDP growth, inflation and interest rates under structural change: a comparison of models with time-varying parameters," Bank of England working papers 450, Bank of England.
    10. Angela Abbate & Sandra Eickmeier & Wolfgang Lemke & Massimiliano Marcellino, 2016. "The Changing International Transmission of Financial Shocks: Evidence from a Classical Time‐Varying FAVAR," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(4), pages 573-601, June.
    11. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Annual Review of Economics, Annual Reviews, vol. 7(1), pages 207-229, August.
    12. Sandra Eickmeier & Tim Ng, 2009. "Forecasting national activity using lots of international predictors: an application to New Zealand," Reserve Bank of New Zealand Discussion Paper Series DP2009/04, Reserve Bank of New Zealand.
    13. Gary Koop & Dimitris Korobilis, 2013. "A new index of financial conditions," Working Papers 1307, University of Strathclyde Business School, Department of Economics.
    14. Hosszú, Zsuzsanna, 2018. "The impact of credit supply shocks and a new Financial Conditions Index based on a FAVAR approach," Economic Systems, Elsevier, vol. 42(1), pages 32-44.
    15. Niall O’Sullivan & Sheng Zhu & Jason Foran, 2019. "Sentiment versus liquidity pricing effects in the cross-section of UK stock returns," Journal of Asset Management, Palgrave Macmillan, vol. 20(4), pages 317-329, July.

  65. Stelios Bekiros & Massimiliano Marcellino, 2011. "The Multiscale Causal Dynamics of Foreign Exchange Markets," Economics Working Papers ECO2011/23, European University Institute.

    Cited by:

    1. Sehgal, Sanjay & Pandey, Piyush & Diesting, Florent, 2017. "Examining dynamic currency linkages amongst South Asian economies: An empirical study," Research in International Business and Finance, Elsevier, vol. 42(C), pages 173-190.
    2. Kitamura, Yoshihiro, 2017. "Simple measures of market efficiency: A study in foreign exchange markets," Japan and the World Economy, Elsevier, vol. 41(C), pages 1-16.
    3. Avdoulas, Christos & Bekiros, Stelios & Boubaker, Sabri, 2016. "Detecting nonlinear dependencies in eurozone peripheral equity markets: A multistep filtering approach," Economic Modelling, Elsevier, vol. 58(C), pages 580-587.
    4. Faria, Gonçalo & Verona, Fabio, 2020. "The yield curve and the stock market: Mind the long run," Journal of Financial Markets, Elsevier, vol. 50(C).
    5. Matthieu Garcin, 2019. "Hurst Exponents And Delampertized Fractional Brownian Motions," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(05), pages 1-26, August.
    6. Gong, Yuting & Ma, Chao & Chen, Qiang, 2022. "Exchange rate dependence and economic fundamentals: A Copula-MIDAS approach," Journal of International Money and Finance, Elsevier, vol. 123(C).
    7. Bekiros, Stelios & Boubaker, Sabri & Nguyen, Duc Khuong & Uddin, Gazi Salah, 2017. "Black swan events and safe havens: The role of gold in globally integrated emerging markets," Journal of International Money and Finance, Elsevier, vol. 73(PB), pages 317-334.
    8. Faria, Gonçalo & Verona, Fabio, 2020. "Frequency-domain information for active portfolio management," Bank of Finland Research Discussion Papers 2/2020, Bank of Finland.
    9. Saba Qureshi & Muhammad Aftab, 2023. "Exchange Rate Interdependence in ASEAN Markets: A Wavelet Analysis," Global Business Review, International Management Institute, vol. 24(6), pages 1180-1204, December.
    10. Mensi, Walid & Hammoudeh, Shawkat & Yoon, Seong-Min, 2014. "Structural breaks and long memory in modeling and forecasting volatility of foreign exchange markets of oil exporters: The importance of scheduled and unscheduled news announcements," International Review of Economics & Finance, Elsevier, vol. 30(C), pages 101-119.
    11. Rémi Odry & Roman Mestre, 2021. "Monetary Policy and Business Cycle Synchronization in Europe," Working Papers hal-04159759, HAL.
    12. Nikola Gradojevic, 2021. "Brexit and foreign exchange market expectations: Could it have been predicted?," Annals of Operations Research, Springer, vol. 297(1), pages 167-189, February.
    13. Caraiani, Petre & Haven, Emmanuel, 2015. "Evidence of multifractality from CEE exchange rates against Euro," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 395-407.
    14. Rahman, Md Lutfur & Troster, Victor & Uddin, Gazi Salah & Yahya, Muhammad, 2022. "Systemic risk contribution of banks and non-bank financial institutions across frequencies: The Australian experience," International Review of Financial Analysis, Elsevier, vol. 79(C).
    15. Syed Jawad Hussain Shahzad & Jose Arreola‐Hernandez & Md Lutfur Rahman & Gazi Salah Uddin & Muhammad Yahya, 2021. "Asymmetric interdependence between currency markets' volatilities across frequencies and time scales," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2436-2457, April.
    16. Aloui, Chaker & Hkiri, Besma & Lau, Marco Chi Keung & Yarovaya, Larisa, 2018. "Information transmission across stock indices and stock index futures: International evidence using wavelet framework," Research in International Business and Finance, Elsevier, vol. 44(C), pages 411-421.
    17. Bilgili, Faik & Kocak, Emrah & Kuskaya, Sevda & Bulut, Umit, 2022. "Co-movements and causalities between ethanol production and corn prices in the USA: New evidence from wavelet transform analysis," Energy, Elsevier, vol. 259(C).
    18. Gazi Salah Uddin & Ahmed Taneem Muzaffar & Mohamed Arouri & Bo Sjö, 2017. "Understanding the Relationship between Inflation and Growth: A Wavelet Transformation Approach in the Case of Bangladesh," Post-Print hal-01653256, HAL.
    19. Markus Vogl, 2022. "Quantitative modelling frontiers: a literature review on the evolution in financial and risk modelling after the financial crisis (2008–2019)," SN Business & Economics, Springer, vol. 2(12), pages 1-69, December.
    20. Stelios Bekiros & Jose Arreola Hernandez & Gazi Salah Uddin & Ahmed Taneem Muzaffar, 2020. "On the predictability of crude oil market: A hybrid multiscale wavelet approach," Post-Print hal-02956380, HAL.
    21. Mehmet Balcilar & Rangan Gupta & Duc Khuong Nguyen & Mark E. Wohar, 2018. "Causal effects of the United States and Japan on Pacific-Rim stock markets: nonparametric quantile causality approach," Applied Economics, Taylor & Francis Journals, vol. 50(53), pages 5712-5727, November.
    22. McNevin, Bruce D. & Nix, Joan, 2018. "The beta heuristic from a time/frequency perspective: A wavelet analysis of the market risk of sectors," Economic Modelling, Elsevier, vol. 68(C), pages 570-585.
    23. Bekiros, Stelios & Nguyen, Duc Khuong & Uddin, Gazi Salah & Sjö, Bo, 2016. "On the time scale behavior of equity-commodity links: Implications for portfolio management," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 41(C), pages 30-46.
    24. Vogl, Markus, 2022. "Controversy in financial chaos research and nonlinear dynamics: A short literature review," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    25. Tiwari, Aviral Kumar & Trabelsi, Nader & Abakah, Emmanuel Joel Aikins & Nasreen, Samia & Lee, Chien-Chiang, 2023. "An empirical analysis of the dynamic relationship between clean and dirty energy markets," Energy Economics, Elsevier, vol. 124(C).
    26. Uddin, Gazi Salah & Bekiros, Stelios & Ahmed, Ali, 2018. "The nexus between geopolitical uncertainty and crude oil markets: An entropy-based wavelet analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 30-39.
    27. Liow, Kim Hiang & Huang, Yuting & Song, Jeonseop, 2019. "Relationship between the United States housing and stock markets: Some evidence from wavelet analysis," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    28. Xiaojie Xu, 2018. "Causal structure among US corn futures and regional cash prices in the time and frequency domain," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(13), pages 2455-2480, October.
    29. Matthieu Garcin, 2018. "Hurst exponents and delampertized fractional Brownian motions," Working Papers hal-01919754, HAL.
    30. Gonçalo Faria & Fabio Verona, 2021. "Time-frequency forecast of the equity premium," Quantitative Finance, Taylor & Francis Journals, vol. 21(12), pages 2119-2135, December.
    31. Nicoló Andrea Caserini & Paolo Pagnottoni, 2022. "Effective transfer entropy to measure information flows in credit markets," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 729-757, October.
    32. Sevda Kuşkaya & Nurhan Toğuç & Faik Bilgili, 2022. "Wavelet coherence analysis and exchange rate movements," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4675-4692, December.
    33. Peterson Owusu Junior & Anokye M. Adam & George Tweneboah, 2017. "Co-movement of real exchange rates in the West African Monetary Zone," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1351807-135, January.
    34. Bouri, Elie & Lucey, Brian & Saeed, Tareq & Vo, Xuan Vinh, 2020. "Extreme spillovers across Asian-Pacific currencies: A quantile-based analysis," International Review of Financial Analysis, Elsevier, vol. 72(C).
    35. Sohel Azad, A.S.M. & Batten, Jonathan A. & Fang, Victor & Wickramanayake, Jayasinghe, 2015. "International swap market contagion and volatility," Economic Modelling, Elsevier, vol. 47(C), pages 355-371.
    36. Al Rababa’a, Abdel Razzaq & Alomari, Mohammad & McMillan, David, 2021. "Multiscale stock-bond correlation: Implications for risk management," Research in International Business and Finance, Elsevier, vol. 58(C).
    37. Jena, Sangram Keshari & Tiwari, Aviral Kumar & Roubaud, David, 2018. "Comovements of gold futures markets and the spot market: A wavelet analysis," Finance Research Letters, Elsevier, vol. 24(C), pages 19-24.
    38. Palazzi, Rafael Baptista & Júnior, Gerson de Souza Raimundo & Klotzle, Marcelo Cabus, 2021. "The dynamic relationship between bitcoin and the foreign exchange market: A nonlinear approach to test causality between bitcoin and currencies," Finance Research Letters, Elsevier, vol. 42(C).
    39. Habimana, Olivier, 2017. "The multiscale relationship between exchange rates and fundamentals differentials: Empirical evidence from Scandinavia," MPRA Paper 75956, University Library of Munich, Germany.

  66. Szabolcs Deak & Lionel Fontagné & Massimiliano Marcellino & Marco Maffezzoli, 2011. "LSM: A DSGE Model for Luxembourg," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00639888, HAL.

    Cited by:

    1. Donal Smith, 2015. "Collateral Constraints and the Interest Rate," Discussion Papers 15/22, Department of Economics, University of York.
    2. Szabolcs Deák & Lionel Fontagné & Marco Maffezzoli & Massimiliano Marcellino, 2012. "The banking and distribution sectors in a small open economy DSGE Model," Working Papers 454, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    3. Tim Schwarzmüller & Nikolai Stähler, 2013. "Reforming the labor market and improving competitiveness: an analysis for Spain using FiMod," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 4(4), pages 437-471, November.
    4. Alban Moura & Kyriacos Lambrias, 2018. "LU-EAGLE: A DSGE model for Luxembourg within the euro area and global economy," BCL working papers 122, Central Bank of Luxembourg.
    5. Yuliya Rychalovska & Massimiliano Marcellino (EUI), 2013. "An estimated DSGE model of a Small Open Economy within the Monetary Union: Forecasting and Structural Analysis," EcoMod2013 5302, EcoMod.
    6. Massimiliano Marcellino & Yuliya Rychalovska, 2014. "Forecasting with a DSGE Model of a Small Open Economy within the Monetary Union," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(5), pages 315-338, August.
    7. Luca Marchiori & Olivier Pierrard, 2012. "LOLA 2.0: Luxembourg OverLapping generation model for policy Analysis," BCL working papers 76, Central Bank of Luxembourg.
    8. Acocella, Nicola & Beqiraj, Elton & Di Bartolomeo, Giovanni & Di Pietro, Marco & Felici, Francesco & Alleva, Giorgio & Di Dio, Fabio & Liseo, Brunero, 2020. "A stochastic estimated version of the Italian dynamic General Equilibrium Model," Economic Modelling, Elsevier, vol. 92(C), pages 339-357.
    9. Luca Marchiori & Olivier Pierrard, 2015. "LOLA 3.0: Luxembourg OverLapping generation model for policy Analysis," BCL working papers 100, Central Bank of Luxembourg.
    10. Donal Smith, 2020. "Collateral Constraints and the Interest Rate," Scottish Journal of Political Economy, Scottish Economic Society, vol. 67(2), pages 137-165, May.
    11. Ibrahima Sangaré, 2019. "Housing sector and optimal macroprudential policy in an estimated DSGE model for Luxembourg," BCL working papers 129, Central Bank of Luxembourg.
    12. Francesco Sergi, 2020. "The Standard Narrative about DSGE Models in Central Banks’ Technical Reports," The European Journal of the History of Economic Thought, Taylor & Francis Journals, vol. 27(2), pages 163-193, March.

  67. Marcellino, Massimiliano & Eickmeier, Sandra & Lemke, Wolfgang, 2011. "The Changing International Transmission of Financial Shocks: Evidence from a Classical Time-Varying FAVAR," CEPR Discussion Papers 8341, C.E.P.R. Discussion Papers.

    Cited by:

    1. Sungurtekin Hallam, Bahar, 2022. "Emerging market responses to external shocks: A cross-country analysis," Economic Modelling, Elsevier, vol. 115(C).
    2. Jorge Lorca, 2021. "Capital Flows and Emerging Markets Fluctuations," Working Papers Central Bank of Chile 898, Central Bank of Chile.
    3. Marcellino, Massimiliano & Eickmeier, Sandra & Lemke, Wolfgang, 2011. "Classical time-varying FAVAR models - Estimation, forecasting and structural analysis," CEPR Discussion Papers 8321, C.E.P.R. Discussion Papers.
    4. Megaritis, Anastasios & Vlastakis, Nikolaos & Triantafyllou, Athanasios, 2021. "Stock market volatility and jumps in times of uncertainty," Journal of International Money and Finance, Elsevier, vol. 113(C).
    5. Alexandra Born & Zeno Enders, 2018. "Global Banking, Trade, and the International Transmission of the Great Recession," CESifo Working Paper Series 6912, CESifo.
    6. Corina SAMAN, 2016. "The Impact of the US and Euro Area Financial Systemic Stress to the Romanian Economy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 170-183, December.
    7. Michal Franta & Jan Libich & Petr Stehlík, 2018. "Tracking Monetary-Fiscal Interactions across Time and Space," International Journal of Central Banking, International Journal of Central Banking, vol. 14(3), pages 167-227, June.
    8. Knut Are Aastveit & Hilde C. Bjørnland & Leif Anders Thorsrud, 2011. "The world is not enough! Small open economies and regional dependence," Working Paper 2011/16, Norges Bank.
    9. Karlsson, Sune & Österholm, Pär, 2018. "Is the US Phillips Curve Stable? Evidence from Bayesian VARs," Working Papers 2018:5, Örebro University, School of Business.
    10. Liu, Wei & Garrett, Ian, 2023. "Regime-dependent effects of macroeconomic uncertainty on realized volatility in the U.S. stock market," Economic Modelling, Elsevier, vol. 128(C).
    11. Eickmeier, Sandra & Ng, Tim, 2011. "How Do Credit Supply Shocks Propagate Internationally? A GVAR approach," CEPR Discussion Papers 8720, C.E.P.R. Discussion Papers.
    12. Prieto, Esteban & Eickmeier, Sandra & Marcellino, Massimiliano, 2013. "Time variation in macro-financial linkages," Discussion Papers 13/2013, Deutsche Bundesbank.
    13. Thanda Sithole & Beatrice D. Simo-Kengne & Modeste Some, 2017. "The role of financial conditions in transmitting external shocks to South Africa," International Economics, CEPII research center, issue 150, pages 36-56.
    14. Lasha Kavtaradze & Manouchehr Mokhtari, 2018. "Factor Models And Time†Varying Parameter Framework For Forecasting Exchange Rates And Inflation: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 302-334, April.
    15. Nikolaos Antonakakis & Harald Badinger, 2012. "Output Volatility, Economic Growth, and Cross-Country Spillovers: New Evidence for the G7 Countries," FIW Working Paper series 098, FIW.
    16. Yves S. Schüler, 2014. "Asymmetric Effects of Uncertainty over the Business Cycle: A Quantile Structural Vector Autoregressive Approach," Working Paper Series of the Department of Economics, University of Konstanz 2014-02, Department of Economics, University of Konstanz.
    17. Michal Franta & Roman Horvath & Marek Rusnak, 2011. "Evaluating Changes in the Monetary Transmission Mechanism in the Czech Republic," Working Papers 2011/13, Czech National Bank.
    18. Galariotis, Emilios & Makrichoriti, Panagiota & Spyrou, Spyros, 2018. "The impact of conventional and unconventional monetary policy on expectations and sentiment," Journal of Banking & Finance, Elsevier, vol. 86(C), pages 1-20.
    19. Guidolin, Massimo & Hansen, Erwin & Pedio, Manuela, 2019. "Cross-asset contagion in the financial crisis: A Bayesian time-varying parameter approach," Journal of Financial Markets, Elsevier, vol. 45(C), pages 83-114.
    20. Vespignani, Joaquin L. & Ratti, Ronald A., 2016. "Not all international monetary shocks are alike for the Japanese economy," Economic Modelling, Elsevier, vol. 52(PB), pages 822-837.
    21. Kose,Ayhan & Lakatos,Csilla & Ohnsorge,Franziska Lieselotte & Stocker,Marc, 2017. "The global role of the U.S. economy: linkages, policies and spillovers," Policy Research Working Paper Series 7962, The World Bank.
    22. Philippe Goulet Coulombe, 2021. "The Macroeconomy as a Random Forest," Working Papers 21-05, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    23. Angela Abbate & Sandra Eickmeier & Wolfgang Lemke & Massimiliano Marcellino, 2016. "The Changing International Transmission of Financial Shocks: Evidence from a Classical Time‐Varying FAVAR," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(4), pages 573-601, June.
    24. Förster, Marcel & Jorra, Markus & Tillmann, Peter, 2014. "The dynamics of international capital flows: Results from a dynamic hierarchical factor model," Journal of International Money and Finance, Elsevier, vol. 48(PA), pages 101-124.
    25. Marcellino, Massimiliano & Carriero, Andrea & Corsello, Francesco, 2019. "The Global Component of Inflation Volatility," CEPR Discussion Papers 13470, C.E.P.R. Discussion Papers.
    26. Boehl, Gregor, 2022. "Monetary policy and speculative asset markets," European Economic Review, Elsevier, vol. 148(C).
    27. Hilde C. Bjørnland & Leif Anders Thorsrud, 2019. "Commodity prices and fiscal policy design: Procyclical despite a rule," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 161-180, March.
    28. Vacca, Valerio Paolo & Bichlmeier, Fabian & Biraschi, Paolo & Boschi, Natalie & Álvarez, Antonio J. Bravo & Di Primio, Luciano & Ebner, André & Hoeretzeder, Silvia & Ballesteros, Elisa Llorente & Mian, 2021. "Measuring the impact of a bank failure on the real economy: an EU-wide analytical framework," ESRB Working Paper Series 122, European Systemic Risk Board.
    29. Anastasios Evgenidis & Costas Siriopoulos, 2015. "What are the International Channels Through Which a US Policy Shock is Transmitted to The World Economies? Evidence from a Time Varying FAVAR," Working Papers 190, Bank of Greece.
    30. Krokida, Styliani-Iris & Makrychoriti, Panagiota & Spyrou, Spyros, 2020. "Monetary policy and herd behavior: International evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 170(C), pages 386-417.
    31. Antonakakis, N. & Badinger, H., 2016. "Economic growth, volatility, and cross-country spillovers: New evidence for the G7 countries," Economic Modelling, Elsevier, vol. 52(PB), pages 352-365.
    32. Valls Pereira, Pedro L. & da Silva Fonseca, Marcelo Gonçalves, 2012. "Credit Shocks and Monetary Policy in Brazil: A Structural Favar Approach," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 32(2), April.
    33. Goodness C. Aye & Mehmet Balcilar & Rangan Gupta, 2020. "The Effectiveness Of Monetary Policy In South Africa Under Inflation Targeting: Evidence from a Time-Varying Factor-Augmented Vector Autoregressive Model," Journal of Developing Areas, Tennessee State University, College of Business, vol. 54(4), pages 55-73, October-D.
    34. Kumar, Ankit & Dash, Pradyumna, 2020. "Changing transmission of monetary policy on disaggregate inflation in India," Economic Modelling, Elsevier, vol. 92(C), pages 109-125.
    35. Jean-François Rouillard, 2015. "International Risk Sharing and Financial Shocks," Cahiers de recherche 15-13, Departement d'économique de l'École de gestion à l'Université de Sherbrooke.
    36. Knut Are Aastveit & Hilde C. Bjornland, 2013. "What drives oil prices? Emerging versus developed economies," CAMA Working Papers 2013-11, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    37. Matthew Greenwood‐Nimmo & Viet Hoang Nguyen & Eliza Wu, 2021. "On the International Spillover Effects of Country‐Specific Financial Sector Bailouts and Sovereign Risk Shocks," The Economic Record, The Economic Society of Australia, vol. 97(317), pages 285-309, June.
    38. Baumeister, Christiane & Hamilton, James D., 2021. "Reprint: Drawing conclusions from structural vector autoregressions identified on the basis of sign restrictions," Journal of International Money and Finance, Elsevier, vol. 114(C).
    39. Cesa-Bianchi, Ambrogio & Sokol, Andrej, 2022. "Financial shocks, credit spreads, and the international credit channel," Journal of International Economics, Elsevier, vol. 135(C).
    40. Beutel, Johannes & Emter, Lorenz & Metiu, Norbert & Prieto, Esteban & Schüler, Yves, 2022. "The global financial cycle and macroeconomic tail risks," Discussion Papers 43/2022, Deutsche Bundesbank.
    41. Takao Asano & Xiaojing Cai & Ryuta Sakemoto, 2023. "Time-varying ambiguity shocks and business cycles," KIER Working Papers 1094, Kyoto University, Institute of Economic Research.
    42. Ellington, Michael & Florackis, Chris & Milas, Costas, 2017. "Liquidity shocks and real GDP growth: Evidence from a Bayesian time-varying parameter VAR," Journal of International Money and Finance, Elsevier, vol. 72(C), pages 93-117.
    43. Aastveit, Knut Are, 2014. "Oil price shocks in a data-rich environment," Energy Economics, Elsevier, vol. 45(C), pages 268-279.
    44. Gent Bajraj & Jorge Lorca & Juan M. Wlasiuk, 2022. "On Foreign Drivers of EMEs Fluctuations," Working Papers Central Bank of Chile 951, Central Bank of Chile.
    45. Kwon, Hyuck-Shin & Bang, Doo Won & Kim, Myeong Hyeon, 2017. "Korean Housing Cycle: Implications for Risk Management (Factor-augmented VAR Approach)," KDI Journal of Economic Policy, Korea Development Institute (KDI), vol. 39(3), pages 43-62.
    46. Ellington, Michael, 2018. "Financial market illiquidity shocks and macroeconomic dynamics: Evidence from the UK," Journal of Banking & Finance, Elsevier, vol. 89(C), pages 225-236.
    47. Yamamoto, Shugo, 2014. "Transmission of US financial and trade shocks to Asian economies: Implications for spillover of the 2007–2009 US financial crisis," The North American Journal of Economics and Finance, Elsevier, vol. 27(C), pages 88-103.
    48. Karlsson, Sune & Österholm, Pär, 2019. "The Relation between the Corporate Bond-Yield Spread and the Real Economy: Stable or TimeVarying?," Working Papers 2019:7, Örebro University, School of Business.
    49. Hilberg, Björn & Grill, Michael & Metiu, Norbert, 2016. "Credit constraints and the international propagation of US financial shocks," Working Paper Series 1954, European Central Bank.
    50. Carrillo Julio A. & García Ana Laura, 2021. "The COVID-19 Economic Crisis in Mexico through the Lens of a Financial Conditions Index," Working Papers 2021-23, Banco de México.
    51. Maldonado, Javier & Ruiz Ortega, Esther, 2017. "Accurate Subsampling Intervals of Principal Components Factors," DES - Working Papers. Statistics and Econometrics. WS 23974, Universidad Carlos III de Madrid. Departamento de Estadística.
    52. Doo Won Bang & HyuckShin Kwon, 2022. "Policy Impact Analysis of Housing Policies Using Housing Cycles," SAGE Open, , vol. 12(3), pages 21582440221, July.
    53. Lena Tonzer, 2013. "Cross-Border Interbank Networks, Banking Risk and Contagion," FIW Working Paper series 129, FIW.
    54. Gary Koop & Dimitris Korobilis, 2013. "A new index of financial conditions," Working Papers 1307, University of Strathclyde Business School, Department of Economics.
    55. Eddie Gerba & Danilo Leiva-Leon, 2020. "Macro-financial interactions in a changing world," Working Papers 2018, Banco de España.
    56. Christiane Baumeister & James D. Hamilton, 2020. "Drawing Conclusions from Structural Vector Autoregressions Identified on the Basis of Sign Restrictions," NBER Working Papers 26606, National Bureau of Economic Research, Inc.
    57. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2020. "The economic drivers of volatility and uncertainty," Temi di discussione (Economic working papers) 1285, Bank of Italy, Economic Research and International Relations Area.
    58. Hosszú, Zsuzsanna, 2018. "The impact of credit supply shocks and a new Financial Conditions Index based on a FAVAR approach," Economic Systems, Elsevier, vol. 42(1), pages 32-44.
    59. Fink, Fabian & Schüler, Yves S., 2015. "The transmission of US systemic financial stress: Evidence for emerging market economies," Journal of International Money and Finance, Elsevier, vol. 55(C), pages 6-26.
    60. Sandra Eickmeier & Wolfgang Lemke & Massimiliano Marcellino, 2015. "Classical time varying factor-augmented vector auto-regressive models—estimation, forecasting and structural analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(3), pages 493-533, June.
    61. Guesmi, Khaled & Makrychoriti, Panagiota & Spyrou, Spyros, 2023. "The relationship between climate risk, climate policy uncertainty, and CO2 emissions: Empirical evidence from the US," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 610-628.
    62. Koester, Gerrit B. & Priesmeier, Christoph, 2015. "The Timing and Responsiveness of Fiscal Policy over the Business Cycle in Germany," MPRA Paper 68412, University Library of Munich, Germany.
    63. Ruch,Franz Ulrich, 2020. "Prospects, Risks, and Vulnerabilities in Emerging and Developing Economies : Lessons from the Past Decade," Policy Research Working Paper Series 9181, The World Bank.
    64. Andrej Sokol & Ambrogio Cesa-Bianchi, 2017. "The International Credit Channel of U.S. Monetary Policy and Financial Shocks," 2017 Meeting Papers 724, Society for Economic Dynamics.
    65. Paolo Gorgi & Siem Jan Koopman & Julia Schaumburg, 2021. "Vector Autoregressions with Dynamic Factor Coefficients and Conditionally Heteroskedastic Errors," Tinbergen Institute Discussion Papers 21-056/III, Tinbergen Institute.

  68. Marcellino, Massimiliano, 2011. "Markov-switching MIDAS models," CEPR Discussion Papers 8234, C.E.P.R. Discussion Papers.

    Cited by:

    1. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    2. Poon, Aubrey & Zhu, Dan, 2022. "Do Recessions Occur Concurrently Across Countries? A Multinomial Logistic Approach," Working Papers 2022:11, Örebro University, School of Business.
    3. Lixiong Yang, 2022. "Threshold mixed data sampling (TMIDAS) regression models with an application to GDP forecast errors," Empirical Economics, Springer, vol. 62(2), pages 533-551, February.
    4. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    5. Fady Barsoum, 2015. "Point and Density Forecasts Using an Unrestricted Mixed-Frequency VAR Model," Working Paper Series of the Department of Economics, University of Konstanz 2015-19, Department of Economics, University of Konstanz.
    6. Reichlin, Lucrezia & Giannone, Domenico & Modugno, Michele & Banbura, Marta, 2012. "Now-casting and the real-time data flow," CEPR Discussion Papers 9112, C.E.P.R. Discussion Papers.
    7. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    8. Xinyu Wang & Cathy Ning, 2022. "A new Markov regime‐switching count time series approach for forecasting initial public offering volumes and detecting issue cycles," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 118-133, January.
    9. Marie Bessec & Othman Bouabdallah, 2015. "Forecasting GDP over the Business Cycle in a Multi-Frequency and Data-Rich Environment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(3), pages 360-384, June.
    10. Ghysels, Eric & Guérin, Pierre & Marcellino, Massimiliano, 2014. "Regime switches in the risk–return trade-off," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 118-138.
    11. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    12. Marcellino, Massimiliano & Foroni, Claudia & Casarin, Roberto & Ravazzolo, Francesco, 2017. "Uncertainty Through the Lenses of A Mixed-Frequency Bayesian Panel Markov Switching Model," CEPR Discussion Papers 12339, C.E.P.R. Discussion Papers.
    13. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    14. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2014. "Combined Density Nowcasting in an uncertain economic environment," Working Paper 2014/17, Norges Bank.
    15. Qiu, Yue, 2020. "Forecasting the Consumer Confidence Index with tree-based MIDAS regressions," Economic Modelling, Elsevier, vol. 91(C), pages 247-256.
    16. Pierre Guérin & Danilo Leiva-Leon, 2015. "Model Averaging in Markov-Switching Models: Predicting National Recessions with Regional Data," Staff Working Papers 15-24, Bank of Canada.
    17. Charfeddine, Lanouar & Klein, Tony & Walther, Thomas, 2018. "Oil Price Changes and U.S. Real GDP Growth: Is this Time Different?," QBS Working Paper Series 2018/03, Queen's University Belfast, Queen's Business School.
    18. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," PSE Working Papers halshs-02262202, HAL.
    19. Fady Barsoum & Sandra Stankiewicz, 2013. "Forecasting GDP Growth Using Mixed-Frequency Models With Switching Regimes," Working Paper Series of the Department of Economics, University of Konstanz 2013-10, Department of Economics, University of Konstanz.
    20. Feng Ma & Chao Liang & Yuanhui Ma & M.I.M. Wahab, 2020. "Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1277-1290, December.
    21. Boriss Siliverstovs, 2019. "Assessing Nowcast Accuracy of US GDP Growth in Real Time: The Role of Booms and Busts," Working Papers 2019/01, Latvijas Banka.
    22. Lynda Khalaf & Maral Kichian & Charles Saunders & Marcel Voia, 2021. "Dynamic panels with MIDAS covariates: Nonlinearity, estimation and fit," Post-Print hal-03528880, HAL.
    23. Ouyang, Ruolan & Chen, Xiang & Fang, Yi & Zhao, Yang, 2022. "Systemic risk of commodity markets: A dynamic factor copula approach," International Review of Financial Analysis, Elsevier, vol. 82(C).
    24. Pan, Zhiyuan & Wang, Qing & Wang, Yudong & Yang, Li, 2018. "Forecasting U.S. real GDP using oil prices: A time-varying parameter MIDAS model," Energy Economics, Elsevier, vol. 72(C), pages 177-187.
    25. Liu, Xiaochun, 2017. "An integrated macro-financial risk-based approach to the stressed capital requirement," Review of Financial Economics, Elsevier, vol. 34(C), pages 86-98.
    26. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2017. "Markov-Switching Three-Pass Regression Filter," Staff Working Papers 17-13, Bank of Canada.
    27. Bjoern Schulte-Tillman & Mawuli Segnon & Bernd Wilfling, 2022. "Financial-market volatility prediction with multiplicative Markov-switching MIDAS components," CQE Working Papers 9922, Center for Quantitative Economics (CQE), University of Muenster.
    28. Michael Funke & Hao Yu & Aaron Mehrota, 2011. "Tracking Chinese CPI inflation in real time," Quantitative Macroeconomics Working Papers 21112, Hamburg University, Department of Economics.
    29. Thomas B Götz & Klemens Hauzenberger, 2021. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure," The Econometrics Journal, Royal Economic Society, vol. 24(3), pages 442-461.
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    1. Shi Yafeng & Ai Chunrong & Yanlong Shi & Ying Tingting & Xu Qunfang, 2023. "Large covariance estimation using a factor model with common and group‐specific factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2217-2248, December.
    2. Utku Özmen, Mustafa & Akçelik, Fatih, 2017. "Asymmetric exchange rate and oil price pass-through in motor fuel market: A microeconometric approach," The Journal of Economic Asymmetries, Elsevier, vol. 15(C), pages 64-75.
    3. Carlomagno, Guillermo & Espasa, Antoni, 2014. "The pairwise approach to model a large set of disaggregates with common trends," DES - Working Papers. Statistics and Econometrics. WS ws141309, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Cristina Conflitti & Matteo Luciani, 2019. "Oil Price Pass-Through into Core Inflation," FEDS Notes 2019-04-30, Board of Governors of the Federal Reserve System (U.S.).
    5. Firouz Fallahi, 2019. "Persistence and stationarity of sectoral energy consumption in the US: A confidence interval approach," Energy & Environment, , vol. 30(5), pages 882-897, August.
    6. Sala, Hector & Trivín, Pedro, 2013. "Labour Market Dynamics in Spanish Regions: Evaluating Asymmetries in Troublesome Times," IZA Discussion Papers 7746, Institute of Labor Economics (IZA).
    7. Alessandro Cantelmo & Giovanni Melina, 2015. "Monetary Policy and the Relative Price of Durable Goods," CESifo Working Paper Series 5328, CESifo.
    8. H. Marques & G. Pino & JdD Tena, 2009. "Regional inflation dynamics using space-time models," Working Paper CRENoS 200915, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    9. Kaufmann, Daniel & Lein, Sarah M., 2013. "Sticky prices or rational inattention – What can we learn from sectoral price data?," European Economic Review, Elsevier, vol. 64(C), pages 384-394.
    10. Guillermo Carlomagno & Nicolas Eterovic & L. G. Hernández-Román, 2023. "Disentangling Demand and Supply Inflation Shocks from Chilean Electronic Payment Data," Working Papers Central Bank of Chile 986, Central Bank of Chile.
    11. Raphael A. Auer & Andrei A. Levchenko & Philip Saure, 2017. "International Inflation Spillovers Through Input Linkages," Working Papers 655, Research Seminar in International Economics, University of Michigan.
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    15. Degui Li & Bin Peng & Songqiao Tang & Weibiao Wu, 2023. "Inference of Grouped Time-Varying Network Vector Autoregression Models," Monash Econometrics and Business Statistics Working Papers 5/23, Monash University, Department of Econometrics and Business Statistics.
    16. Kapetanios, George & Serlenga, Laura & Shin, Yongcheol, 2021. "Estimation and inference for multi-dimensional heterogeneous panel datasets with hierarchical multi-factor error structure," Journal of Econometrics, Elsevier, vol. 220(2), pages 504-531.
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    20. Elena Deryugina & Natalia Karlova & Alexey Ponomarenko & Anna Tsvetkova, 2019. "The role of regional and sectoral factors in Russian inflation developments," Economic Change and Restructuring, Springer, vol. 52(4), pages 453-474, November.
    21. Pino, Gabriel & Tena Horrillo, Juan de Dios & Espasa, Antoni, 2013. "Forecasting disaggregates by sectors and regions : the case of inflation in the euro area and Spain," DES - Working Papers. Statistics and Econometrics. WS ws130807, Universidad Carlos III de Madrid. Departamento de Estadística.
    22. Firouz Fallahi, 2020. "Persistence and unit root in $$\text {CO}_{2}$$CO2 emissions: evidence from disaggregated global and regional data," Empirical Economics, Springer, vol. 58(5), pages 2155-2179, May.
    23. Chong, Terence Tai Leung & Wu, Zhang, 2018. "Price Rigidity in China: Empirical Results at Home and Abroad," MPRA Paper 92013, University Library of Munich, Germany.
    24. Paweł Gajewski, 2017. "Sources of Regional Inflation in Poland," Eastern European Economics, Taylor & Francis Journals, vol. 55(3), pages 261-276, May.
    25. Jörg Breitung & Sandra Eickmeier, 2014. "Analyzing business and financial cycles using multi-level factor models," CAMA Working Papers 2014-43, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    26. Carlomagno, Guillermo & Espasa, Antoni, 2016. "Discovering common trends in a large set of disaggregates: statistical procedures and their properties," DES - Working Papers. Statistics and Econometrics. WS ws1519, Universidad Carlos III de Madrid. Departamento de Estadística.
    27. Espasa, Antoni & Senra, Eva, 2017. "22 Years of inflation assessment and forecasting experience at the bulletin of EU & US inflation and macroeconomic analysis," DES - Working Papers. Statistics and Econometrics. WS 24678, Universidad Carlos III de Madrid. Departamento de Estadística.
    28. Antoni Espasa & Eva Senra, 2017. "Twenty-Two Years of Inflation Assessment and Forecasting Experience at the Bulletin of EU & US Inflation and Macroeconomic Analysis," Econometrics, MDPI, vol. 5(4), pages 1-28, October.
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    4. Joshua C. C. Chan & Xuewen Yu, 2022. "Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility," Papers 2206.08438, arXiv.org.
    5. Tallman, Ellis W. & Zaman, Saeed, 2020. "Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy," International Journal of Forecasting, Elsevier, vol. 36(2), pages 373-398.
    6. Salzmann, Leonard, 2020. "The Impact of Uncertainty and Financial Shocks in Recessions and Booms," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224588, Verein für Socialpolitik / German Economic Association.
    7. Anna Staszewska-Bystrova & Peter Winker, 2014. "Measuring Forecast Uncertainty of Corporate Bond Spreads by Bonferroni-Type Prediction Bands," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 6(2), pages 89-104, June.
    8. Scott Brave & R. Andrew Butters & Alejandro Justiniano, 2016. "Forecasting Economic Activity with Mixed Frequency Bayesian VARs," Working Paper Series WP-2016-5, Federal Reserve Bank of Chicago.
    9. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
    10. Christiane Baumeister & Dimitris Korobilis & Thomas K. Lee, 2022. "Energy Markets and Global Economic Conditions," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 828-844, October.
    11. Minchul Shin & Molin Zhong, 2015. "Does Realized Volatility Help Bond Yield Density Prediction?," Finance and Economics Discussion Series 2015-115, Board of Governors of the Federal Reserve System (U.S.).
    12. Cross, Jamie L. & Hou, Chenghan & Koop, Gary & Poon, Aubrey, 2023. "Large stochastic volatility in mean VARs," Journal of Econometrics, Elsevier, vol. 236(1).
    13. Edward S. Knotek & Saeed Zaman, 2017. "Financial Nowcasts and Their Usefulness in Macroeconomic Forecasting," Working Papers (Old Series) 1702, Federal Reserve Bank of Cleveland.
    14. Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 366-380, July.
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    25. Billio, Monica & Casarin, Roberto & Rossini, Luca, 2019. "Bayesian nonparametric sparse VAR models," Journal of Econometrics, Elsevier, vol. 212(1), pages 97-115.
    26. Giacomo Rella, 2021. "The Fed, housing and household debt over time," Department of Economics University of Siena 850, Department of Economics, University of Siena.
    27. Camehl, Annika, 2023. "Penalized estimation of panel vector autoregressive models: A panel LASSO approach," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1185-1204.
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    81. Prüser Jan & Hanck Christoph, 2021. "A Comparison of Approaches to Select the Informativeness of Priors in BVARs," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 241(4), pages 501-525, August.
    82. Dominik Bertsche & Ralf Brüggemann & Christian Kascha, 2023. "Directed graphs and variable selection in large vector autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(2), pages 223-246, March.
    83. Fu, Bowen, 2023. "Measuring the trend real interest rate in a data-rich environment," Journal of Economic Dynamics and Control, Elsevier, vol. 147(C).
    84. Dimitris Korobilis., 2015. "Prior selection for panel vector autoregressions," Working Papers 2015_10, Business School - Economics, University of Glasgow.
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    86. Garegnani, Lorena & Gómez Aguirre, Maximiliano, 2018. "Forecasting Inflation in Argentina," IDB Publications (Working Papers) 8940, Inter-American Development Bank.
    87. Ho, Paul, 2023. "Global robust Bayesian analysis in large models," Journal of Econometrics, Elsevier, vol. 235(2), pages 608-642.
    88. Todd E. Clark & Michael W. McCracken, 2013. "Evaluating the accuracy of forecasts from vector autoregressions," Working Papers 2013-010, Federal Reserve Bank of St. Louis.
    89. Mike Tsionas & Marwan Izzeldin & Lorenzo Trapani, 2019. "Bayesian estimation of large dimensional time varying VARs using copulas," Papers 1912.12527, arXiv.org.
    90. Демешев Борис Борисович & Малаховская Оксана Анатольевна, 2016. "Макроэкономическое Прогнозирование С Помощью Bvar Литтермана," Higher School of Economics Economic Journal Экономический журнал Высшей школы экономики, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», vol. 20(4), pages 691-710.
    91. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2016. "Structural analysis with Multivariate Autoregressive Index models," Journal of Econometrics, Elsevier, vol. 192(2), pages 332-348.
    92. Tomasz Wozniak, 2016. "Rare Events and Risk Perception: Evidence from Fukushima Accident," Department of Economics - Working Papers Series 2021, The University of Melbourne.
    93. Antonio Pacifico, 2021. "Structural Panel Bayesian VAR with Multivariate Time-Varying Volatility to Jointly Deal with Structural Changes, Policy Regime Shifts, and Endogeneity Issues," Econometrics, MDPI, vol. 9(2), pages 1-35, May.
    94. Ganics, Gergely & Odendahl, Florens, 2021. "Bayesian VAR forecasts, survey information, and structural change in the euro area," International Journal of Forecasting, Elsevier, vol. 37(2), pages 971-999.
    95. Berg, Tim Oliver, 2015. "Multivariate Forecasting with BVARs and DSGE Models," MPRA Paper 62405, University Library of Munich, Germany.
    96. Gianluca Cubadda & Stefano Grassi & Barbara Guardabascio, 2024. "The Time-Varying Multivariate Autoregressive Index Model," CEIS Research Paper 571, Tor Vergata University, CEIS, revised 10 Jan 2024.
    97. Koop, Gary, 2014. "Forecasting with dimension switching VARs," International Journal of Forecasting, Elsevier, vol. 30(2), pages 280-290.
    98. Gu, Xin & Zhu, Zixiang & Yu, Minli, 2021. "The macro effects of GPR and EPU indexes over the global oil market—Are the two types of uncertainty shock alike?," Energy Economics, Elsevier, vol. 100(C).
    99. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Large Vector Autoregressions with Stochastic Volatility and Flexible Priors," Working Papers (Old Series) 1617, Federal Reserve Bank of Cleveland.
    100. Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022. "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers 2212.03471, arXiv.org, revised Jul 2023.
    101. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    102. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    103. Kalli, Maria & Griffin, Jim E., 2018. "Bayesian nonparametric vector autoregressive models," Journal of Econometrics, Elsevier, vol. 203(2), pages 267-282.
    104. Mikhail Mamonov & Anna Pestova, 2021. ""Sorry, You're Blocked." Economic Effects of Financial Sanctions on the Russian Economy," CERGE-EI Working Papers wp704, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    105. Joshua C. C. Chan, 2022. "Asymmetric conjugate priors for large Bayesian VARs," Quantitative Economics, Econometric Society, vol. 13(3), pages 1145-1169, July.
    106. Ashwin Madhou & Tayushma Sewak & Imad Moosa & Vikash Ramiah, 2017. "GDP nowcasting: application and constraints in a small open developing economy," Applied Economics, Taylor & Francis Journals, vol. 49(38), pages 3880-3890, August.
    107. Joohun Han & John N. Ng’ombe, 2023. "The relation between wheat, soybean, and hemp acreage: a Bayesian time series analysis," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 11(1), pages 1-12, December.
    108. Feuerriegel, Stefan & Gordon, Julius, 2019. "News-based forecasts of macroeconomic indicators: A semantic path model for interpretable predictions," European Journal of Operational Research, Elsevier, vol. 272(1), pages 162-175.
    109. Katleho Makatjane & Ntebogang Moroke, 2021. "Predicting Extreme Daily Regime Shifts in Financial Time Series Exchange/Johannesburg Stock Exchange—All Share Index," IJFS, MDPI, vol. 9(2), pages 1-18, March.
    110. Florian Huber & Gary Koop, 2023. "Subspace shrinkage in conjugate Bayesian vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 556-576, June.
    111. Michael P. Clements & Ana Beatriz Galvão, 2023. "Density forecasting with Bayesian Vector Autoregressive models under macroeconomic data uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 164-185, March.
    112. Chevillon, Guillaume, 2017. "Robustness of Multistep Forecasts and Predictive Regressions at Intermediate and Long Horizons," ESSEC Working Papers WP1710, ESSEC Research Center, ESSEC Business School.
    113. Dimitrios P. Louzis, 2019. "Steady‐state modeling and macroeconomic forecasting quality," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 285-314, March.
    114. Pablo Guerróon‐Quintana & Molin Zhong, 2023. "Macroeconomic forecasting in times of crises," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 295-320, April.
    115. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano, 2019. "Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors," Journal of Econometrics, Elsevier, vol. 212(1), pages 137-154.
    116. Götz, Thomas B. & Hauzenberger, Klemens, 2018. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure," Discussion Papers 40/2018, Deutsche Bundesbank.
    117. Piergiorgio Alessandri & Haroon Mumtaz, 2017. "Online Appendix to "Financial conditions and density forecasts for US output and inflation"," Online Appendices 14-103, Review of Economic Dynamics.
    118. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2020. "The economic drivers of volatility and uncertainty," Temi di discussione (Economic working papers) 1285, Bank of Italy, Economic Research and International Relations Area.
    119. Kenneth Beauchemin & Saeed Zaman, 2011. "A medium scale forecasting model for monetary policy," Working Papers (Old Series) 1128, Federal Reserve Bank of Cleveland.
    120. Antonio Pacifico, 2022. "Structural Compressed Panel VAR with Stochastic Volatility: A Robust Bayesian Model Averaging Procedure," Econometrics, MDPI, vol. 10(3), pages 1-24, July.
    121. Dellaportas, Petros & Tsionas, Mike G., 2019. "Importance sampling from posterior distributions using copula-like approximations," Journal of Econometrics, Elsevier, vol. 210(1), pages 45-57.
    122. Wang,Dieter & Andree,Bo Pieter Johannes & Chamorro Elizondo,Andres Fernando & Spencer,Phoebe Girouard, 2020. "Stochastic Modeling of Food Insecurity," Policy Research Working Paper Series 9413, The World Bank.
    123. Mokinski, Frieder, 2017. "A severity function approach to scenario selection," Discussion Papers 34/2017, Deutsche Bundesbank.
    124. Simone Emiliozzi & Elisa Guglielminetti & Michele Loberto, 2018. "Forecasting house prices in Italy," Questioni di Economia e Finanza (Occasional Papers) 463, Bank of Italy, Economic Research and International Relations Area.
    125. Tim Oliver Berg & Steffen Henzel, 2014. "Point and Density Forecasts for the Euro Area Using Bayesian VARs," CESifo Working Paper Series 4711, CESifo.
    126. Mihaela Simionescu, 2016. "Foreign Direct Investment and Sustainable Development. A Regional Approach for Romania," Working Papers of Macroeconomic Modelling Seminar 162702, Institute for Economic Forecasting.
    127. Clark, Todd E. & Doh, Taeyoung, 2014. "Evaluating alternative models of trend inflation," International Journal of Forecasting, Elsevier, vol. 30(3), pages 426-448.
    128. Mikhail Mamonov & Anna Pestova, 2023. "The Price of War: Macroeconomic and Cross-Sectional Effects of Sanctions on Russia," CERGE-EI Working Papers wp756, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    129. Akbar, Muhammad & Iqbal, Farhan & Noor, Farzana, 2019. "Bayesian analysis of dynamic linkages among gold price, stock prices, exchange rate and interest rate in Pakistan," Resources Policy, Elsevier, vol. 62(C), pages 154-164.
    130. Salzmann, Leonard, 2019. "The Impact of Uncertainty and Financial Shocks in Recessions and Booms," EconStor Preprints 206691, ZBW - Leibniz Information Centre for Economics.
    131. Domit, Sílvia & Monti, Francesca & Sokol, Andrej, 2016. "A Bayesian VAR benchmark for COMPASS," Bank of England working papers 583, Bank of England.
    132. Hartwig, Benny, 2022. "Bayesian VARs and prior calibration in times of COVID-19," Discussion Papers 52/2022, Deutsche Bundesbank.
    133. Cobb, Marcus P A, 2017. "Aggregate Density Forecasting from Disaggregate Components Using Large VARs," MPRA Paper 76849, University Library of Munich, Germany.
    134. Jesús Crespo Cuaresma & Martin Feldkircher & Florian Huber, 2014. "Forecasting with Bayesian Global Vector Autoregressive Models: A Comparison of Priors," Working Papers 189, Oesterreichische Nationalbank (Austrian Central Bank).

  71. Marcellino, Massimiliano & Musso, Alberto, 2010. "The Forecasting Performance of Real Time Estimates of the Euro Area Output Gap," CEPR Discussion Papers 7763, C.E.P.R. Discussion Papers.

    Cited by:

    1. Martin Gächter & Aleksandra Riedl & Doris Ritzberger-Grünwald, 2012. "Business Cycle Synchronization in the Euro Area and the Impact of the Financial Crisis," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue 2, pages 33-60.
    2. Jed Armstrong, 2015. "The Reserve Bank of New Zealand’s output gap indicator suite and its real-time properties," Reserve Bank of New Zealand Analytical Notes series AN2015/08, Reserve Bank of New Zealand.
    3. Massimiliano Marcellino & Alberto Musso, 2010. "the Reliability of Real Time Estimates of the EURO Area Output Gap," Economics Working Papers ECO2010/06, European University Institute.
    4. Raffinot, Thomas, 2017. "Interest-Rates-Free Monetary Policy Rule," Working Papers 06898, George Mason University, Mercatus Center.
    5. Bańbura, Marta & Bobeica, Elena, 2023. "Does the Phillips curve help to forecast euro area inflation?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 364-390.
    6. Marcin Wolski, 2016. "Welfare-theoretic Optimal Policies in a New-Keynesian Economy with Heterogeneous Regions: Any Role for Financial Integration?," Journal of Common Market Studies, Wiley Blackwell, vol. 54(3), pages 742-761, May.
    7. Guérin, Pierre & Maurin, Laurent & Mohr, Matthias, 2015. "Trend-Cycle Decomposition Of Output And Euro Area Inflation Forecasts: A Real-Time Approach Based On Model Combination," Macroeconomic Dynamics, Cambridge University Press, vol. 19(2), pages 363-393, March.
    8. Czudaj, Robert, 2011. "P-star in times of crisis - Forecasting inflation for the euro area," Economic Systems, Elsevier, vol. 35(3), pages 390-407, September.
    9. Schumacher, Christian & Marcellino, Massimiliano & Kuzin, Vladimir, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
    10. Punnoose Jacob & Finn Robinson, 2019. "Suite as! Augmenting the Reserve Bank’s output gap indicator suite," Reserve Bank of New Zealand Analytical Notes series AN2019/08, Reserve Bank of New Zealand.
    11. Kuusi, Tero, 2018. "Does the structural budget balance guide fiscal policy pro-cyclically? Evidence from the Finnish Great Depression of the 1990s," MPRA Paper 84829, University Library of Munich, Germany.
    12. Otmar Issing, 2010. "The development of monetary policy in the 20th century – some reflections," Working Paper Research 186, National Bank of Belgium.
    13. Agnello, Luca & Castro, Vítor & Sousa, Ricardo M., 2023. "A quest between fiscal and market discipline," Economic Modelling, Elsevier, vol. 119(C).
    14. Sergei Aliukov & Jan Buleca, 2022. "Comparative Multidimensional Analysis of the Current State of European Economies Based on the Complex of Macroeconomic Indicators," Mathematics, MDPI, vol. 10(5), pages 1-29, March.
    15. Nicolas Pinkwart, 2013. "Quantifying The European Central Bank'S Interest Rate Smoothing Behavior," Manchester School, University of Manchester, vol. 81(4), pages 470-492, July.
    16. Kukk Merike & Staehr Karsten, 2015. "Enhanced Fiscal Governance in the European Union: The Fiscal Compact," TalTech Journal of European Studies, Sciendo, vol. 5(1), pages 73-92, February.
    17. Bjørn Eraker & Ching Wai (Jeremy) Chiu & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2015. "Bayesian Mixed Frequency VARs," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 698-721.
    18. João Sousa Andrade & António Portugal Duarte, 2014. "Output-gaps in the PIIGS Economies: An Ingredient of a Greek Tragedy," GEMF Working Papers 2014-06, GEMF, Faculty of Economics, University of Coimbra.
    19. Christian Gayer & Bertrand Marc, 2018. "A ‘New Modesty’? Level Shifts in Survey Data and the Decreasing Trend of ‘Normal’ Growth," European Economy - Discussion Papers 083, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.

  72. Marcellino, Massimiliano & Kapetanios, George & Carriero, Andrea, 2010. "Forecasting Government Bond Yields with Large Bayesian VARs," CEPR Discussion Papers 7796, C.E.P.R. Discussion Papers.

    Cited by:

    1. Haroon Mumtaz & Alexandra Solovyeva & Elena Vasilieva, 2012. "Asset prices, credit and the Russian economy," Joint Research Papers 1, Centre for Central Banking Studies, Bank of England.
    2. Cristina Fuentes-Albero & Leonardo Melosi, 2011. "Methods for Computing Marginal Data Densities from the Gibbs Output," Departmental Working Papers 201131, Rutgers University, Department of Economics.
    3. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Bayesian VARs: Specification Choices and Forecast Accuracy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 46-73, January.
    4. Habrov, Vladimir, 2012. "Optimization of portfolio management based on vector autoregression models and multivariate volatility models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 28(4), pages 35-62.
    5. Hajer Ben Romdhane & Nahed Ben Tanfous, 2017. "Conditional FAVAR and scenario analysis for a large data: case of Tunisia," IHEID Working Papers 15-2017, Economics Section, The Graduate Institute of International Studies.
    6. Caio Almeida & Axel Simonsen & José Valentim Vicente, 2012. "Forecasting Bond Yields with Segmented Term Structure Models," Working Papers Series 288, Central Bank of Brazil, Research Department.
    7. Kapetanios, George & Mumtaz, Haroon & Stevens, Ibrahim & Theodoridis, Konstantinos, 2012. "Assessing the economy-wide effects of quantitative easing," Bank of England working papers 443, Bank of England.
    8. Simon Gilchrist & Egon Zakrajsek & Cristina Fuentes Albero & Dario Caldara, 2013. "On the Identification of Financial and Uncertainty Shocks," 2013 Meeting Papers 965, Society for Economic Dynamics.

  73. Marcellino, Massimiliano & Galvão, Ana Beatriz, 2010. "Endogenous Monetary Policy Regimes and the Great Moderation," CEPR Discussion Papers 7827, C.E.P.R. Discussion Papers.

    Cited by:

    1. Marcellino, Massimiliano & Eickmeier, Sandra & Lemke, Wolfgang, 2011. "Classical time-varying FAVAR models - Estimation, forecasting and structural analysis," CEPR Discussion Papers 8321, C.E.P.R. Discussion Papers.
    2. Jouchi Nakajima, 2011. "Monetary Policy Transmission under Zero Interest Rates: An Extended Time-Varying Parameter Vector Autoregression Approach," IMES Discussion Paper Series 11-E-08, Institute for Monetary and Economic Studies, Bank of Japan.
    3. Jouchi Nakajima & Mike West, 2013. "Bayesian Analysis of Latent Threshold Dynamic Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 151-164, April.
    4. Sergei Seleznev, 2019. "Truncated priors for tempered hierarchical Dirichlet process vector autoregression," Bank of Russia Working Paper Series wps47, Bank of Russia.
    5. Jolejole-Foreman, Maria Christina & Mallory, Mindy L. & Baylis, Katherine R., 2013. "Impact of Wheat and Rice Export Ban on Indian Market Integration," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150595, Agricultural and Applied Economics Association.
    6. Ahmad Yamin & Donayre Luiggi, 2016. "Outliers and persistence in threshold autoregressive processes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 20(1), pages 37-56, February.
    7. Sandra Eickmeier & Wolfgang Lemke & Massimiliano Marcellino, 2015. "Classical time varying factor-augmented vector auto-regressive models—estimation, forecasting and structural analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(3), pages 493-533, June.

  74. Marcellino, Massimiliano & Musso, Alberto, 2010. "The Reliability of Real Time Estimates of the Euro Area Output Gap," CEPR Discussion Papers 7716, C.E.P.R. Discussion Papers.

    Cited by:

    1. Cronin, David & McQuinn, Kieran, 2020. "Are official forecasts of output growth in the EU still biased? Evidence from stability and convergence programmes and the European Commission’s Spring forecasts," Papers WP681, Economic and Social Research Institute (ESRI).
    2. Martin Gächter & Aleksandra Riedl & Doris Ritzberger-Grünwald, 2012. "Business Cycle Synchronization in the Euro Area and the Impact of the Financial Crisis," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue 2, pages 33-60.
    3. Jed Armstrong, 2015. "The Reserve Bank of New Zealand’s output gap indicator suite and its real-time properties," Reserve Bank of New Zealand Analytical Notes series AN2015/08, Reserve Bank of New Zealand.
    4. L. Marattin & S. Meraglia, 2015. "Potential Output and Fiscal Rules in a Monetary Union under Asymmetric Information," Working Papers wp1018, Dipartimento Scienze Economiche, Universita' di Bologna.
    5. Katharina Glass & Ulrich Fritsche, 2015. "Real-time Macroeconomic Data and Uncertainty," Macroeconomics and Finance Series 201406, University of Hamburg, Department of Socioeconomics.
    6. Cláudia Duarte & José R. Maria & Sharmin Sazedj, 2019. "Trends and cycles under changing economic conditions," Working Papers w201918, Banco de Portugal, Economics and Research Department.
    7. Massimiliano Marcellino & Alberto Musso, 2010. "the Reliability of Real Time Estimates of the EURO Area Output Gap," Economics Working Papers ECO2010/06, European University Institute.
    8. Jan Capek, 2014. "Historical Analysis of Monetary Policy Reaction Functions: Do Real-Time Data Matter?," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 64(6), pages 457-475, December.
    9. Aastveit, Knut Are & Trovik, Tørres, 2014. "Estimating the output gap in real time: A factor model approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 54(2), pages 180-193.
    10. Onur Ince & David H. Papell, 2013. "The (Un)Reliability of Real-Time Output Gap Estimates with Revised Data," Working Papers 13-02, Department of Economics, Appalachian State University.
    11. Kristian Jönsson, 2020. "Real-time US GDP gap properties using Hamilton’s regression-based filter," Empirical Economics, Springer, vol. 59(1), pages 307-314, July.
    12. Boysen-Hogrefe, Jens, 2014. "Konjunkturbereinigung der Länder: Eine Quasi-Echtzeitanalyse am Beispiel Schleswig-Holsteins," Kiel Discussion Papers 538, Kiel Institute for the World Economy (IfW Kiel).
    13. Cronin, David & McQuinn, Kieran, 2021. "Are official forecasts of output growth in the EU still biased?," Journal of Policy Modeling, Elsevier, vol. 43(2), pages 337-349.
    14. Marcell Göttert & Timo Wollmershäuser, 2021. "Survey-Based Structural Budget Balances," EconPol Working Paper 59, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    15. Feldkircher, Martin, 2014. "The determinants of vulnerability to the global financial crisis 2008 to 2009: Credit growth and other sources of risk," Journal of International Money and Finance, Elsevier, vol. 43(C), pages 19-49.
    16. Raffinot, Thomas, 2017. "Interest-Rates-Free Monetary Policy Rule," Working Papers 06898, George Mason University, Mercatus Center.
    17. de Carvalho, Miguel & Rua, António, 2017. "Real-time nowcasting the US output gap: Singular spectrum analysis at work," International Journal of Forecasting, Elsevier, vol. 33(1), pages 185-198.
    18. L. Marattin & S. Meraglia, 2016. "Potential Output and Fiscal Rules in a Monetary Union under Asymmetric Information 2nd ed," Working Papers wp1063, Dipartimento Scienze Economiche, Universita' di Bologna.
    19. Bańbura, Marta & Bobeica, Elena, 2023. "Does the Phillips curve help to forecast euro area inflation?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 364-390.
    20. Marcin Wolski, 2016. "Welfare-theoretic Optimal Policies in a New-Keynesian Economy with Heterogeneous Regions: Any Role for Financial Integration?," Journal of Common Market Studies, Wiley Blackwell, vol. 54(3), pages 742-761, May.
    21. Guérin, Pierre & Maurin, Laurent & Mohr, Matthias, 2015. "Trend-Cycle Decomposition Of Output And Euro Area Inflation Forecasts: A Real-Time Approach Based On Model Combination," Macroeconomic Dynamics, Cambridge University Press, vol. 19(2), pages 363-393, March.
    22. Rochelle M. Edge & Jeremy B. Rudd, 2016. "Real-Time Properties of the Federal Reserve's Output Gap," The Review of Economics and Statistics, MIT Press, vol. 98(4), pages 785-791, October.
    23. Hieu Thanh Nguyen & Hiep Ngoc Luu & Ngoc Ha Do, 2021. "The dynamic relationship between greenfield investments, cross-border M&As, domestic investment and economic growth in Vietnam," Economic Change and Restructuring, Springer, vol. 54(4), pages 1065-1089, November.
    24. Xueting Yu & Yuhan Zhu & Guangming Lv, 2020. "Analysis of the Impact of China’s GDP Data Revision on Monetary Policy from the Perspective of Uncertainty," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(6), pages 1251-1274, May.
    25. Lorenzo Burlon & Paolo D'Imperio, 2019. "The euro-area output gap through the lens of a DSGE model," Questioni di Economia e Finanza (Occasional Papers) 477, Bank of Italy, Economic Research and International Relations Area.
    26. Czudaj, Robert, 2011. "P-star in times of crisis - Forecasting inflation for the euro area," Economic Systems, Elsevier, vol. 35(3), pages 390-407, September.
    27. Schumacher, Christian & Marcellino, Massimiliano & Kuzin, Vladimir, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
    28. Kai Carstensen & Felix Kießner & Thies Rossian, 2023. "Estimation of the TFP Gap for the Largest Five EMU Countries," CESifo Working Paper Series 10245, CESifo.
    29. Jens Boysen-Hogrefe, 2015. "Konjunkturbereinigungsverfahren der Länder: Eine Quasi-Echtzeitanalyse am Beispiel Schleswig-Holsteins," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 9(1), pages 41-57, April.
    30. Galimberti, Jaqueson K. & Moura, Marcelo L., 2016. "Improving the reliability of real-time output gap estimates using survey forecasts," International Journal of Forecasting, Elsevier, vol. 32(2), pages 358-373.
    31. Punnoose Jacob & Finn Robinson, 2019. "Suite as! Augmenting the Reserve Bank’s output gap indicator suite," Reserve Bank of New Zealand Analytical Notes series AN2019/08, Reserve Bank of New Zealand.
    32. González-Astudillo, Manuel, 2019. "An output gap measure for the euro area: Exploiting country-level and cross-sectional data heterogeneity," European Economic Review, Elsevier, vol. 120(C).
    33. Kuusi, Tero, 2018. "Does the structural budget balance guide fiscal policy pro-cyclically? Evidence from the Finnish Great Depression of the 1990s," MPRA Paper 84829, University Library of Munich, Germany.
    34. Jens Boysen‐Hogrefe, 2015. "Monetary Aggregates to Improve Early Output Gap Estimates in the Euro Area: An Empirical Assessment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(7), pages 533-542, November.
    35. Otmar Issing, 2010. "The development of monetary policy in the 20th century – some reflections," Working Paper Research 186, National Bank of Belgium.
    36. Agnello, Luca & Castro, Vítor & Sousa, Ricardo M., 2023. "A quest between fiscal and market discipline," Economic Modelling, Elsevier, vol. 119(C).
    37. Lise Pichette & Marie-Noëlle Robitaille & Mohanad Salameh & Pierre St-Amant, 2018. "Dismiss the Gap? A Real-Time Assessment of the Usefulness of Canadian Output Gaps in Forecasting Inflation," Staff Working Papers 18-10, Bank of Canada.
    38. Garratt, Anthony & Mitchell, James & Vahey, Shaun, 2013. "Measuring Output Gap Nowcast Uncertainty," EMF Research Papers 01, Economic Modelling and Forecasting Group.
    39. Sergei Aliukov & Jan Buleca, 2022. "Comparative Multidimensional Analysis of the Current State of European Economies Based on the Complex of Macroeconomic Indicators," Mathematics, MDPI, vol. 10(5), pages 1-29, March.
    40. Nicolas Pinkwart, 2013. "Quantifying The European Central Bank'S Interest Rate Smoothing Behavior," Manchester School, University of Manchester, vol. 81(4), pages 470-492, July.
    41. Marek Rusnak, 2013. "Nowcasting Czech GDP in Real Time," Working Papers 2013/06, Czech National Bank.
    42. Morley, James & Palenzuela, Diego Rodriguez & Sun, Yiqiao & Wong, Benjamin, 2022. "Estimating the Euro Area output gap using multivariate information and addressing the COVID-19 pandemic," Working Paper Series 2716, European Central Bank.
    43. Cronin, David & McInerney, Niall, 2023. "Official fiscal forecasts in EU member states under the European Semester and Fiscal Compact – An empirical assessment," European Journal of Political Economy, Elsevier, vol. 76(C).
    44. Paloviita, Maritta & Haavio, Markus & Jalasjoki, Pirkka & Kilponen, Juha, 2017. "What does "below, but close to, two percent" mean? Assessing the ECB's reaction function with real time data," Bank of Finland Research Discussion Papers 29/2017, Bank of Finland.
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    47. Rünstler, Gerhard & Balfoussia, Hiona & Burlon, Lorenzo & Buss, Ginters & Comunale, Mariarosaria & De Backer, Bruno & Dewachter, Hans & Guarda, Paolo & Haavio, Markus & Hindrayanto, Irma & Iskrev, Nik, 2018. "Real and financial cycles in EU countries - Stylised facts and modelling implications," Occasional Paper Series 205, European Central Bank.
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    51. Dominik Bernhofer & Octavio Fernández-Amador & Martin Gächter & Friedrich Sindermann, 2014. "Finance, Potential Output and the Business Cycle: Empirical Evidence from Selected Advanced and CESEE Economies," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 2, pages 52-75.
    52. Romain Bouis & Ane Kathrine Christensen & Boris Cournède, 2013. "Deleveraging: Challenges, Progress and Policies," OECD Economics Department Working Papers 1077, OECD Publishing.
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    54. Maritta Paloviita & Markus Haavio & Pirkka Jalasjoki & Juha Kilponen, 2021. "What Does "Below, but Close to, 2 Percent" Mean? Assessing the ECB's Reaction Function with Real-Time Data," International Journal of Central Banking, International Journal of Central Banking, vol. 17(2), pages 125-169, June.
    55. Quast, Josefine & Wolters, Maik H., 2019. "Reliable Real-time Output Gap Estimates Based on a Modified Hamilton Filter," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203535, Verein für Socialpolitik / German Economic Association.
    56. Dominik Bernhofer & Octavio Fernández-Amador & Martin Gächter & Friedrich Sindermann, 2014. "Finance, potential output and the business cycle," Chapters, in: Ewald Nowotny & Doris Ritzberger-Grünwald & Peter Backé (ed.), Financial Cycles and the Real Economy, chapter 14, pages 235-264, Edward Elgar Publishing.
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    59. Kukk Merike & Staehr Karsten, 2015. "Enhanced Fiscal Governance in the European Union: The Fiscal Compact," TalTech Journal of European Studies, Sciendo, vol. 5(1), pages 73-92, February.
    60. Bjørn Eraker & Ching Wai (Jeremy) Chiu & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2015. "Bayesian Mixed Frequency VARs," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 698-721.
    61. Ringa Raudla & James W. Douglas, 2021. "Structural Budget Balance as a Fiscal Rule in the European Union—Good, Bad, or Ugly?," Public Budgeting & Finance, Wiley Blackwell, vol. 41(1), pages 121-141, March.
    62. Bofinger, Peter & Schnabel, Isabel & Feld, Lars P. & Schmidt, Christoph M. & Wieland, Volker, 2017. "Für eine zukunftsorientierte Wirtschaftspolitik. Jahresgutachten 2017/18 [Towards a Forward-Looking Economic Policy. Annual Report 2017/18]," Annual Economic Reports / Jahresgutachten, German Council of Economic Experts / Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung, volume 127, number 201718.
    63. Ley, Eduardo & Misch, Florian, 2013. "Real-time macro monitoring and fiscal policy," Policy Research Working Paper Series 6303, The World Bank.
    64. Breuer, Sebastian & Elstner, Steffen, 2017. "Die Wachstumsperspektiven der deutschen Wirtschaft vor dem Hintergrund des demografischen Wandels: Die Mittelfristprojektion des Sachverständigenrates," Working Papers 07/2017, German Council of Economic Experts / Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung.
    65. Amy Y. Guisinger & Michael T. Owyang & Hannah Shell, 2018. "Comparing Measures of Potential Output," Review, Federal Reserve Bank of St. Louis, vol. 100(4), pages 297-316.
    66. Burlon, Lorenzo & D’Imperio, Paolo, 2020. "Reliable real-time estimates of the euro-area output gap," Journal of Macroeconomics, Elsevier, vol. 64(C).
    67. Julien Champagne & Guillaume Poulin‐Bellisle & Rodrigo Sekkel, 2018. "The Real‐Time Properties of the Bank of Canada's Staff Output Gap Estimates," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(6), pages 1167-1188, September.
    68. Ramis Khabibullin, 2019. "What measures of real economic activity slack are helpful for forecasting Russian inflation?," Bank of Russia Working Paper Series wps50, Bank of Russia.
    69. Ms. Burcu Hacibedel & Pierre Mandon & Ms. Priscilla S Muthoora & Nathalie Pouokam, 2019. "Inequality in Good and Bad Times: A Cross-Country Approach," IMF Working Papers 2019/020, International Monetary Fund.
    70. Plödt, Martin & Reicher, Claire, 2014. "Estimating simple fiscal policy reaction functions for the euro area countries," Kiel Working Papers 1899, Kiel Institute for the World Economy (IfW Kiel).
    71. Kempkes, Gerhard, 2012. "Cyclical adjustment in fiscal rules: Some evidence on real-time bias for EU-15 countries," Discussion Papers 15/2012, Deutsche Bundesbank.
    72. Finn Robinson & Jamie Culling & Gael Price, 2019. "Evaluating indicators of labour market capacity in New Zealand," Reserve Bank of New Zealand Analytical Notes series AN2019/09, Reserve Bank of New Zealand.
    73. Mr. Jiaqian Chen & Lucyna Gornicka, 2020. "Measuring Output Gap: Is It Worth Your Time?," IMF Working Papers 2020/024, International Monetary Fund.
    74. Christian Gayer & Bertrand Marc, 2018. "A ‘New Modesty’? Level Shifts in Survey Data and the Decreasing Trend of ‘Normal’ Growth," European Economy - Discussion Papers 083, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    75. Jaqueson K. Galimberti & Marcelo L. Moura, 2011. "Improving the reliability of real-time Hodrick-Prescott filtering using survey forecasts," Centre for Growth and Business Cycle Research Discussion Paper Series 159, Economics, The University of Manchester.
    76. International Monetary Fund, 2014. "Russian Federation: Selected Issues," IMF Staff Country Reports 2014/176, International Monetary Fund.
    77. Ricci-Risquete, Alejandro & Ramajo, Julián & de Castro, Francisco, 2016. "Do Spanish fiscal regimes follow the euro-area trends? Evidence from Markov-Switching fiscal rules," Economic Modelling, Elsevier, vol. 59(C), pages 484-494.

  75. Marcellino, Massimiliano & Knüppel, Malte & Jordà , Òscar, 2010. "Empirical Simultaneous Confidence Regions for Path-Forecasts," CEPR Discussion Papers 7797, C.E.P.R. Discussion Papers.

    Cited by:

    1. Kung, Ko-Lun & MacMinn, Richard D. & Kuo, Weiyu & Tsai, Chenghsien Jason, 2022. "Multi-population mortality modeling: When the data is too much and not enough," Insurance: Mathematics and Economics, Elsevier, vol. 103(C), pages 41-55.
    2. Sinclair, Tara M. & Stekler, H.O., 2013. "Examining the quality of early GDP component estimates," International Journal of Forecasting, Elsevier, vol. 29(4), pages 736-750.
    3. Tara Sinclair & Herman O. Stekler & Warren Carnow, 2012. "A New Approach For Evaluating Economic Forecasts," Working Papers 2012-2, The George Washington University, Institute for International Economic Policy.
    4. Svetlana Makarova, 2014. "Risk and Uncertainty: Macroeconomic Perspective," UCL SSEES Economics and Business working paper series 129, UCL School of Slavonic and East European Studies (SSEES).
    5. Hans Christian Müller-Dröge & Tara M. Sinclair & Herman O. Stekler, 2014. "Evaluating Forecasts Of A Vector Of Variables: A German Forecasting Competition," Working Papers 2014-004, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    6. David F. Hendry & Felix Pretis, 2020. "Analyzing Differences between Scenarios," Economics Papers 2020-W05, Economics Group, Nuffield College, University of Oxford.
    7. Lee, Seohyun, 2017. "Three essays on uncertainty: real and financial effects of uncertainty shocks," MPRA Paper 83617, University Library of Munich, Germany.
    8. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    9. Constantin Burgi, 2016. "What Do We Lose When We Average Expectations?," Working Papers 2016-013, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    10. Sinclair, Tara M. & Stekler, H.O. & Carnow, Warren, 2015. "Evaluating a vector of the Fed’s forecasts," International Journal of Forecasting, Elsevier, vol. 31(1), pages 157-164.
    11. Filippeli, Thomai & Harrison, Richard & Theodoridis, Konstantinos, 2018. "DSGE-based priors for BVARs and quasi-Bayesian DSGE estimation," Bank of England working papers 716, Bank of England.
    12. Knüppel, Malte, 2014. "Forecast-error-based estimation of forecast uncertainty when the horizon is increased," Discussion Papers 40/2014, Deutsche Bundesbank.
    13. Tara M. Sinclair & H.O. Stekler, 2011. "Differences in Early GDP Component Estimates Between Recession and Expansion," Working Papers 2011-05, The George Washington University, Institute for International Economic Policy.
    14. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Michael Wolf & Dan Wunderli, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 352-376, May.
    15. Thomai Filippeli, 2011. "Theoretical Priors for BVAR Models & Quasi-Bayesian DSGE Model Estimation," 2011 Meeting Papers 396, Society for Economic Dynamics.
    16. Diego Fresoli, 2022. "Bootstrap VAR forecasts: The effect of model uncertainties," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 279-293, March.
    17. Filippeli, Thomai & Harrison, Richard & Theodoridis, Konstantinos, 2018. "DSGE-based Priors for BVARs & Quasi-Bayesian DSGE Estimation," Cardiff Economics Working Papers E2018/5, Cardiff University, Cardiff Business School, Economics Section.
    18. Michael Wolf & Dan Wunderli, 2012. "Bootstrap joint prediction regions," ECON - Working Papers 064, Department of Economics - University of Zurich, revised May 2013.
    19. Gonçalves Mazzeu, Joao Henrique & Ruiz Ortega, Esther & Veiga, Helena, 2015. "Model uncertainty and the forecast accuracy of ARMA models: A survey," DES - Working Papers. Statistics and Econometrics. WS ws1508, Universidad Carlos III de Madrid. Departamento de Estadística.

  76. Marcellino, Massimiliano & Musso, Alberto, 2010. "Real time estimates of the euro area output gap: reliability and forecasting performance," Working Paper Series 1157, European Central Bank.

    Cited by:

    1. Martin Gächter & Aleksandra Riedl & Doris Ritzberger-Grünwald, 2012. "Business Cycle Synchronization in the Euro Area and the Impact of the Financial Crisis," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue 2, pages 33-60.
    2. Massimiliano Marcellino & Alberto Musso, 2010. "the Reliability of Real Time Estimates of the EURO Area Output Gap," Economics Working Papers ECO2010/06, European University Institute.
    3. Raffinot, Thomas, 2017. "Interest-Rates-Free Monetary Policy Rule," Working Papers 06898, George Mason University, Mercatus Center.
    4. Bańbura, Marta & Bobeica, Elena, 2023. "Does the Phillips curve help to forecast euro area inflation?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 364-390.
    5. Marcin Wolski, 2016. "Welfare-theoretic Optimal Policies in a New-Keynesian Economy with Heterogeneous Regions: Any Role for Financial Integration?," Journal of Common Market Studies, Wiley Blackwell, vol. 54(3), pages 742-761, May.
    6. Guérin, Pierre & Maurin, Laurent & Mohr, Matthias, 2015. "Trend-Cycle Decomposition Of Output And Euro Area Inflation Forecasts: A Real-Time Approach Based On Model Combination," Macroeconomic Dynamics, Cambridge University Press, vol. 19(2), pages 363-393, March.
    7. Czudaj, Robert, 2011. "P-star in times of crisis - Forecasting inflation for the euro area," Economic Systems, Elsevier, vol. 35(3), pages 390-407, September.
    8. Schumacher, Christian & Marcellino, Massimiliano & Kuzin, Vladimir, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
    9. Dr. James Mitchell, 2009. "Measuring Output Gap Uncertainty," National Institute of Economic and Social Research (NIESR) Discussion Papers 342, National Institute of Economic and Social Research.
    10. Domenico Giannone & Jérôme Henry & Magdalena Lalik & Michèle Modugno, 2010. "An Area Wide Real Time Data Base for the Euro Area," Working Papers ECARES ECARES 2010-026, ULB -- Universite Libre de Bruxelles.
    11. Punnoose Jacob & Finn Robinson, 2019. "Suite as! Augmenting the Reserve Bank’s output gap indicator suite," Reserve Bank of New Zealand Analytical Notes series AN2019/08, Reserve Bank of New Zealand.
    12. Kuusi, Tero, 2018. "Does the structural budget balance guide fiscal policy pro-cyclically? Evidence from the Finnish Great Depression of the 1990s," MPRA Paper 84829, University Library of Munich, Germany.
    13. Otmar Issing, 2010. "The development of monetary policy in the 20th century – some reflections," Working Paper Research 186, National Bank of Belgium.
    14. Agnello, Luca & Castro, Vítor & Sousa, Ricardo M., 2023. "A quest between fiscal and market discipline," Economic Modelling, Elsevier, vol. 119(C).
    15. Garratt, Anthony & Mitchell, James & Vahey, Shaun, 2013. "Measuring Output Gap Nowcast Uncertainty," EMF Research Papers 01, Economic Modelling and Forecasting Group.
    16. Sergei Aliukov & Jan Buleca, 2022. "Comparative Multidimensional Analysis of the Current State of European Economies Based on the Complex of Macroeconomic Indicators," Mathematics, MDPI, vol. 10(5), pages 1-29, March.
    17. Nicolas Pinkwart, 2013. "Quantifying The European Central Bank'S Interest Rate Smoothing Behavior," Manchester School, University of Manchester, vol. 81(4), pages 470-492, July.
    18. Lucian Croitoru, 2014. "Will there be Deflation and Current Account Surpluses?," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 5-21, October.
    19. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area," Discussion Paper Series 1: Economic Studies 2009,07, Deutsche Bundesbank.
    20. Kukk Merike & Staehr Karsten, 2015. "Enhanced Fiscal Governance in the European Union: The Fiscal Compact," TalTech Journal of European Studies, Sciendo, vol. 5(1), pages 73-92, February.
    21. Bjørn Eraker & Ching Wai (Jeremy) Chiu & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2015. "Bayesian Mixed Frequency VARs," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 698-721.
    22. João Sousa Andrade & António Portugal Duarte, 2014. "Output-gaps in the PIIGS Economies: An Ingredient of a Greek Tragedy," GEMF Working Papers 2014-06, GEMF, Faculty of Economics, University of Coimbra.
    23. Christian Gayer & Bertrand Marc, 2018. "A ‘New Modesty’? Level Shifts in Survey Data and the Decreasing Trend of ‘Normal’ Growth," European Economy - Discussion Papers 083, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.

  77. Marcellino, Massimiliano & Kapetanios, George, 2010. "Factor-GMM Estimation with Large Sets of Possibly Weak Instruments," CEPR Discussion Papers 7726, C.E.P.R. Discussion Papers.

    Cited by:

    1. Natalia Bailey & George Kapetanios & M. Hashem Pesaran, 2012. "Exponent of Cross-sectional Dependence: Estimation and Inference," CESifo Working Paper Series 3722, CESifo.
    2. Gründler, Klaus & Scheuermeyer, Philipp, 2018. "Growth effects of inequality and redistribution: What are the transmission channels?," Journal of Macroeconomics, Elsevier, vol. 55(C), pages 293-313.
    3. Norkutė, Milda & Sarafidis, Vasilis & Yamagata, Takashi & Cui, Guowei, 2021. "Instrumental variable estimation of dynamic linear panel data models with defactored regressors and a multifactor error structure," Journal of Econometrics, Elsevier, vol. 220(2), pages 416-446.
    4. Pesaran, M. Hashem & Tosetti, Elisa, 2007. "Large Panels with Common Factors and Spatial Correlations," IZA Discussion Papers 3032, Institute of Labor Economics (IZA).
    5. Carrasco, Marine & Tchuente, Guy, 2015. "Regularized LIML for many instruments," Journal of Econometrics, Elsevier, vol. 186(2), pages 427-442.
    6. Tong, Eric, 2017. "US monetary policy and global financial stability," Research in International Business and Finance, Elsevier, vol. 39(PA), pages 466-485.
    7. Marcellino, Massimiliano & Kapetanios, George & Khalaf, Lynda, 2015. "Factor based identification-robust inference in IV regressions," CEPR Discussion Papers 10390, C.E.P.R. Discussion Papers.
    8. Sara Markowitz & Erik Nesson & Joshua Robinson, 2010. "The Effects of Employment on Influenza Rates," NBER Working Papers 15796, National Bureau of Economic Research, Inc.
    9. Saman Banafti & Tae-Hwy Lee, 2022. "Inferential Theory for Granular Instrumental Variables in High Dimensions," Papers 2201.06605, arXiv.org, revised Sep 2023.
    10. Hansen, Christian & Kozbur, Damian, 2014. "Instrumental variables estimation with many weak instruments using regularized JIVE," Journal of Econometrics, Elsevier, vol. 182(2), pages 290-308.
    11. Guo, Xiao & Chen, Yu & Tang, Cheng Yong, 2023. "Information criteria for latent factor models: A study on factor pervasiveness and adaptivity," Journal of Econometrics, Elsevier, vol. 233(1), pages 237-250.
    12. A. Belloni & D. Chen & V. Chernozhukov & C. Hansen, 2012. "Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain," Econometrica, Econometric Society, vol. 80(6), pages 2369-2429, November.
    13. Gachet, Ivan & Maldonado, Diego & Pérez, Wilson, 2008. "Determinantes de la Inflación en una Economía Dolarizada: El Caso Ecuatoriano [Determinants of Inflation in a Dollarized Economy: The Case of Ecuador]," MPRA Paper 17101, University Library of Munich, Germany.
    14. Canarella, Giorgio & Miller, Stephen M., 2018. "The determinants of growth in the U.S. information and communication technology (ICT) industry: A firm-level analysis," Economic Modelling, Elsevier, vol. 70(C), pages 259-271.
    15. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2008. "A Shrinkage Instrumental Variable Estimator for Large Datasets," Working Papers 626, Queen Mary University of London, School of Economics and Finance.
    16. Bontempi, Maria Elena & Mammi, Irene, 2012. "A strategy to reduce the count of moment conditions in panel data GMM," MPRA Paper 40720, University Library of Munich, Germany.
    17. Hubrich, Kirstin & Marcellino, Massimiliano & Beck, Günter, 2011. "On the importance of sectoral and regional shocks for price-setting," CEPR Discussion Papers 8357, C.E.P.R. Discussion Papers.
    18. Abeer Elshennawy & Mohammed Bouaddi, 2021. "Sources of firm-level heterogeneity in labour productivity in Egypt’s manufacturing sector," Empirical Economics, Springer, vol. 60(5), pages 2589-2612, May.
    19. Sophocles Mavroeidis & Mikkel Plagborg-Møller & James H. Stock, 2014. "Empirical Evidence on Inflation Expectations in the New Keynesian Phillips Curve," Journal of Economic Literature, American Economic Association, vol. 52(1), pages 124-188, March.
    20. Omer Bayar, 2022. "Reducing large datasets to improve the identification of estimated policy rules," Empirical Economics, Springer, vol. 63(1), pages 113-140, July.
    21. Boot, Tom & Nibbering, Didier, 2019. "Forecasting using random subspace methods," Journal of Econometrics, Elsevier, vol. 209(2), pages 391-406.
    22. Beck, Guenter W. & Hubrich, Kirstin & Marcellino, Massimiliano, 2009. "On the importance of sectoral shocks for price-setting," CFS Working Paper Series 2009/32, Center for Financial Studies (CFS).
    23. Jan J.J. Groen & George Kapetanios, 2008. "Revisiting Useful Approaches to Data-Rich Macroeconomic Forecasting," Working Papers 624, Queen Mary University of London, School of Economics and Finance.
    24. Declan French, 2018. "Financial strain in the United Kingdom," Oxford Economic Papers, Oxford University Press, vol. 70(1), pages 163-182.
    25. Emna Trabelsi, 2022. "Macroprudential Transparency and Price Stability in Emerging and Developing Countries," Journal of Central Banking Theory and Practice, Central bank of Montenegro, vol. 11(1), pages 105-129.
    26. Ieva Skarda, 2016. "The Political Economy of Foreign Aid Effectiveness," Discussion Papers 16/12, Department of Economics, University of York.
    27. Mullings, Robert & Mahabir, Aruneema, 2018. "Growth by Destination: The Role of Trade in Africa’s Recent Growth Episode," World Development, Elsevier, vol. 102(C), pages 243-261.
    28. Emna Trabelsi, 2019. "Do independence and transparency matter for bank development? A new lookup on emerging and developing countries," Post-Print hal-02162780, HAL.
    29. Njindan Iyke, Bernard, 2016. "Does Trade Openness Matter for Economic Growth in the CEE Countries?," MPRA Paper 78869, University Library of Munich, Germany.
    30. Kapetanios, George & Marcellino, Massimiliano, 2010. "Factor-GMM estimation with large sets of possibly weak instruments," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2655-2675, November.
    31. Alexander Chudik & M. Hashem Pesaran & Elisa Tosetti, 2011. "Weak and strong cross‐section dependence and estimation of large panels," Econometrics Journal, Royal Economic Society, vol. 14, pages 45-90, February.
    32. Kapetanios, George & Marcellino, Massimiliano, 2010. "Cross-sectional averaging and instrumental variable estimation with many weak instruments," Economics Letters, Elsevier, vol. 108(1), pages 36-39, July.
    33. Kwok Tong Soo, 2015. "Innovation across cities," Working Papers 100098721, Lancaster University Management School, Economics Department.
    34. Takeshima, Hiroyuki & Liu, Yanyan, 2020. "Smallholder mechanization induced by yield-enhancing biological technologies: Evidence from Nepal and Ghana," Agricultural Systems, Elsevier, vol. 184(C).
    35. Dima Bogdan & Dima Ştefana Maria, 2017. "Does Corporate Tax Burden Affect Growth? Evidences from OECD Countries," Journal of Heterodox Economics, Sciendo, vol. 4(2), pages 51-80, December.
    36. Ludovica Gambaro & Guido Neidhöfer & C. Katharina Spieß, 2019. "The Effect of Early Childhood Education and Care Services on the Social Integration of Refugee Families," Discussion Papers of DIW Berlin 1828, DIW Berlin, German Institute for Economic Research.
    37. Milda Norkute, 2015. "Can the sectoral New Keynesian Phillips curve explain inflation dynamics in the Euro Area?," Empirical Economics, Springer, vol. 49(4), pages 1191-1216, December.
    38. Onatski, Alexei, 2015. "Asymptotic analysis of the squared estimation error in misspecified factor models," Journal of Econometrics, Elsevier, vol. 186(2), pages 388-406.
    39. Wenxi Lu, 2018. "FDI, Service imports and Export development," School of Economics and Public Policy Working Papers 2018-05, University of Adelaide, School of Economics and Public Policy.
    40. Guy Tchuente, 2016. "Estimation of social interaction models using regularization," Studies in Economics 1607, School of Economics, University of Kent.
    41. Yongfu Huang & Muhammad G. Quibria, 2015. "The global partnership for sustainable development," Natural Resources Forum, Blackwell Publishing, vol. 39(3-4), pages 157-174, August.
    42. Fajeau, Maxime, 2021. "Too much finance or too many weak instruments?," International Economics, Elsevier, vol. 165(C), pages 14-36.
    43. Hubert Paul, 2017. "Qualitative and quantitative central bank communication and inflation expectations," The B.E. Journal of Macroeconomics, De Gruyter, vol. 17(1), pages 1-41, January.
    44. Maldonado, Javier & Ruiz Ortega, Esther, 2017. "Accurate Subsampling Intervals of Principal Components Factors," DES - Working Papers. Statistics and Econometrics. WS 23974, Universidad Carlos III de Madrid. Departamento de Estadística.
    45. Mirza, Harun & Storjohann, Lidia, 2011. "Making a Weak Instrument Set Stronger: Factor-Based Estimation of the Taylor Rule," Bonn Econ Discussion Papers 13/2011, University of Bonn, Bonn Graduate School of Economics (BGSE).
    46. Panagiota Makrychoriti & Fotios Pasiouras & Menelaos Tasiou, 2022. "Financial stress and economic growth: The moderating role of trust," Kyklos, Wiley Blackwell, vol. 75(1), pages 48-74, February.
    47. Scheffel, Eric Michael, 2012. "Political uncertainty in a data-rich environment," MPRA Paper 37318, University Library of Munich, Germany.
    48. Guy Tchuente, 2019. "Weak Identification and Estimation of Social Interaction Models," Papers 1902.06143, arXiv.org.
    49. Ng Serena & Bai Jushan, 2009. "Selecting Instrumental Variables in a Data Rich Environment," Journal of Time Series Econometrics, De Gruyter, vol. 1(1), pages 1-34, April.
    50. Paul Hubert, 2014. "Disentangling qualitative and quantitative central bank influence," SciencePo Working papers Main hal-01098464, HAL.
    51. Panagiota Papadimitri & Ansgar Wohlschlegel, 2020. "Lobbying and Enforcement: Theory and Application to Bank Regulation," Working Papers 2020-01, Swansea University, School of Management.
    52. M. E. Bontempi & I. Mammi, 2014. "pca2: implementing a strategy to reduce the instrument count in panel GMM," Working Papers wp960, Dipartimento Scienze Economiche, Universita' di Bologna.
    53. Travaglini, Guido, 2010. "Supervised Principal Components and Factor Instrumental Variables. An Application to Violent CrimeTrends in the US, 1982-2005," MPRA Paper 22077, University Library of Munich, Germany.
    54. Hao-Chang Yang & Ferry Syarifuddin & Chun-Ping Chang & Hai-Jie Wang, 2022. "The Impact of Exchange Rate Futures Fluctuations on Macroeconomy: Evidence from Ten Trading Market," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 58(8), pages 2300-2313, June.
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    56. Claudio Detotto & Sauveur Giannoni & Claire Goavec, 2017. "Does good governance attract tourists?," Working Papers 002, Laboratoire Lieux, Identités, eSpaces et Activités (LISA).
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    1. Castle, Jennifer L. & Doornik, Jurgen A. & Hendry, David F., 2021. "Modelling non-stationary ‘Big Data’," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1556-1575.
    2. Claudio Morana, 2010. "Heteroskedastic Factor Vector Autoregressive Estimation of Persistent and Non Persistent Processes Subject to Structural Breaks," ICER Working Papers - Applied Mathematics Series 36-2010, ICER - International Centre for Economic Research.
    3. Carlomagno, Guillermo & Espasa, Antoni, 2014. "The pairwise approach to model a large set of disaggregates with common trends," DES - Working Papers. Statistics and Econometrics. WS ws141309, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Galbraith, John W. & Zinde-Walsh, Victoria, 2020. "Simple and reliable estimators of coefficients of interest in a model with high-dimensional confounding effects," Journal of Econometrics, Elsevier, vol. 218(2), pages 609-632.
    5. Chris Bloor & Troy Matheson, 2010. "Analysing shock transmission in a data-rich environment: a large BVAR for New Zealand," Empirical Economics, Springer, vol. 39(2), pages 537-558, October.
    6. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    7. Francisco Corona & Graciela González-Farías & Pedro Orraca, 2017. "A dynamic factor model for the Mexican economy: are common trends useful when predicting economic activity?," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 26(1), pages 1-35, December.
    8. Goulet Coulombe, Philippe & Leroux, Maxime & Stevanovic, Dalibor & Surprenant, Stéphane, 2021. "Macroeconomic data transformations matter," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1338-1354.
    9. Manisha Pradhananga, 2016. "Financialization and the rise in co-movement of commodity prices," International Review of Applied Economics, Taylor & Francis Journals, vol. 30(5), pages 547-566, September.
    10. Mahamadou Roufahi Tankari & Anatole Goundan, 2018. "Nontraded food commodity spatial price transmission: evidence from the Niger millet market," Agricultural Economics, International Association of Agricultural Economists, vol. 49(2), pages 147-156, March.
    11. Rangan Gupta & Alain Kabundi & Stephen M. Miller & Josine Uwilingiye, 2011. "Using Large Data Sets to Forecast Sectoral Employment," Working papers 2011-02, University of Connecticut, Department of Economics, revised Aug 2012.
    12. Buss, Ginters, 2010. "A note on GDP now-/forecasting with dynamic versus static factor models along a business cycle," MPRA Paper 22147, University Library of Munich, Germany.
    13. Norman R. Swanson & Weiqi Xiong, 2018. "Big data analytics in economics: What have we learned so far, and where should we go from here?," Canadian Journal of Economics, Canadian Economics Association, vol. 51(3), pages 695-746, August.
    14. Banerjee, Anindya & Marcellino, Massimiliano, 2008. "Factor-augmented Error Correction Models," CEPR Discussion Papers 6707, C.E.P.R. Discussion Papers.
    15. Corradi, Valentina & Swanson, Norman R., 2014. "Testing for structural stability of factor augmented forecasting models," Journal of Econometrics, Elsevier, vol. 182(1), pages 100-118.
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    18. Bušs, Ginters, 2009. "Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach," MPRA Paper 16684, University Library of Munich, Germany.
    19. Giovanni MELINA & Stefania VILLA, 2012. "Fiscal policy and lending relationships," Working Papers of Department of Economics, Leuven ces12.06, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
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    21. Smeekes, Stephan & Wijler, Etiënne, 2016. "Macroeconomic Forecasting Using Penalized Regression Methods," Research Memorandum 039, Maastricht University, Graduate School of Business and Economics (GSBE).
    22. Smeekes, Stephan & Wijler, Etienne, 2021. "An automated approach towards sparse single-equation cointegration modelling," Journal of Econometrics, Elsevier, vol. 221(1), pages 247-276.
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    24. Corona, Francisco & Orraca, Pedro, 2016. "Remittances in Mexico and their unobserved components," DES - Working Papers. Statistics and Econometrics. WS 22674, Universidad Carlos III de Madrid. Departamento de Estadística.
    25. Kyle E. Binder & Mohsen Pourahmadi & James W. Mjelde, 2020. "The role of temporal dependence in factor selection and forecasting oil prices," Empirical Economics, Springer, vol. 58(3), pages 1185-1223, March.
    26. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA forecast averaging - predicting day-ahead and intraday electricity prices," WORking papers in Management Science (WORMS) WORMS/20/02, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    27. Pallara, Kevin, 2016. "The dynamic effects of government spending: a FAVAR approach," MPRA Paper 92283, University Library of Munich, Germany.
    28. Leo Krippner & Sandra Eickmeier & Julia von Borstel, 2015. "The interest rate pass-through in the euro area during the sovereign debt crisis," Reserve Bank of New Zealand Discussion Paper Series DP2015/03, Reserve Bank of New Zealand.
    29. Duangnate, Kannika & Mjelde, James W., 2017. "Comparison of data-rich and small-scale data time series models generating probabilistic forecasts: An application to U.S. natural gas gross withdrawals," Energy Economics, Elsevier, vol. 65(C), pages 411-423.
    30. Dedu, Vasile & Stoica, Tiberiu, 2014. "The Impact of Monetaru Policy on the Romanian Economy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 71-86, June.
    31. Ruiz Ortega, Esther & Poncela, Pilar, 2015. "Small versus big-data factor extraction in Dynamic Factor Models: An empirical assessment," DES - Working Papers. Statistics and Econometrics. WS ws1502, Universidad Carlos III de Madrid. Departamento de Estadística.
    32. Uniejewski, Bartosz & Maciejowska, Katarzyna, 2023. "LASSO principal component averaging: A fully automated approach for point forecast pooling," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1839-1852.
    33. Corona, Francisco & Poncela, Pilar & Ruiz Ortega, Esther, 2017. "Estimating non-stationary common factors : Implications for risk sharing," DES - Working Papers. Statistics and Econometrics. WS 24585, Universidad Carlos III de Madrid. Departamento de Estadística.
    34. Hyun Hak Kim, 2013. "Forecasting Macroeconomic Variables Using Data Dimension Reduction Methods: The Case of Korea," Working Papers 2013-26, Economic Research Institute, Bank of Korea.
    35. Smith, Ron P. & Gylfi, Zoega, 2006. "Global Factors, Unemployment Adjustment and the Natural Rate," Kiel Working Papers 1367, Kiel Institute for the World Economy (IfW Kiel).
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    37. Barhoumi, K. & Darné, O. & Ferrara, L., 2013. "Dynamic Factor Models: A review of the Literature ," Working papers 430, Banque de France.
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    39. Stoupos, Nikolaos & Nikas, Christos & Kiohos, Apostolos, 2023. "Turkey: From a thriving economic past towards a rugged future? - An empirical analysis on the Turkish financial markets," Emerging Markets Review, Elsevier, vol. 54(C).
    40. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05, Department of Economics, University of Birmingham.
    41. John W. Galbraith & Victoria Zinde-Walsh, 2011. "Partially Dimension-Reduced Regressions with Potentially Infinite-Dimensional Processes," CIRANO Working Papers 2011s-57, CIRANO.
    42. László Békési & Lorant Kaszab & Szabolcs Szentmihályi, 2017. "The EAGLE model for Hungary - a global perspective," MNB Working Papers 2017/7, Magyar Nemzeti Bank (Central Bank of Hungary).
    43. Claudia Godbout & Marco J. Lombardi, 2012. "Short-Term Forecasting of the Japanese Economy Using Factor Models," Staff Working Papers 12-7, Bank of Canada.
    44. Maldonado, Javier & Ruiz Ortega, Esther, 2017. "Accurate Subsampling Intervals of Principal Components Factors," DES - Working Papers. Statistics and Econometrics. WS 23974, Universidad Carlos III de Madrid. Departamento de Estadística.
    45. In Choi & Hanbat Jeong, 2020. "Differencing versus nondifferencing in factor‐based forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 728-750, September.
    46. Kurz-Kim, Jeong-Ryeol, 2018. "A note on the predictive power of survey data in nowcasting euro area GDP," Discussion Papers 10/2018, Deutsche Bundesbank.
    47. Scheffel, Eric Michael, 2012. "Political uncertainty in a data-rich environment," MPRA Paper 37318, University Library of Munich, Germany.
    48. Gao, Zhaoxing & Tsay, Ruey S., 2021. "Modeling high-dimensional unit-root time series," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1535-1555.
    49. Nicoletta Pashourtidou & Christos Papamichael & Charalampos Karagiannakis, 2018. "Forecasting economic activity in sectors of the Cypriot economy," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 12(2), pages 24-66, December.
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    1. Stylianos Asimakopoulos & Joan Paredes & Thomas Warmedinger, 2020. "Real‐Time Fiscal Forecasting Using Mixed‐Frequency Data," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(1), pages 369-390, January.
    2. Claudio, João C. & Heinisch, Katja & Holtemöller, Oliver, 2019. "Nowcasting East German GDP growth: A MIDAS approach," IWH Discussion Papers 24/2019, Halle Institute for Economic Research (IWH).
    3. Qian, Hang, 2012. "Essays on statistical inference with imperfectly observed data," ISU General Staff Papers 201201010800003618, Iowa State University, Department of Economics.
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    5. Christiane Baumeister & Pierre Guérin, 2020. "A Comparison of Monthly Global Indicators for Forecasting Growth," CESifo Working Paper Series 8656, CESifo.
    6. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    7. Winkelried, Diego, 2012. "Predicting quarterly aggregates with monthly indicators," Working Papers 2012-023, Banco Central de Reserva del Perú.
    8. Luci Alessi & Eric Ghysels & Luca Onorante & Richard Peach & Simon M. Potter, 2014. "Central bank macroeconomic forecasting during the global financial crisis: the European Central Bank and Federal Reserve Bank of New York experiences," Staff Reports 680, Federal Reserve Bank of New York.
    9. Özer Karagedikli & Murat Özbilgin, 2019. "Mixed in New Zealand: Nowcasting Labour Markets with MIDAS," Reserve Bank of New Zealand Analytical Notes series AN2019/04, Reserve Bank of New Zealand.
    10. Markus Heinrich & Magnus Reif, 2018. "Forecasting using mixed-frequency VARs with time-varying parameters," ifo Working Paper Series 273, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    11. Raquel Nadal Cesar Gonçalves, 2022. "Nowcasting Brazilian GDP with Electronic Payments Data," Working Papers Series 564, Central Bank of Brazil, Research Department.
    12. Edward S. Knotek & Saeed Zaman, 2017. "Financial Nowcasts and Their Usefulness in Macroeconomic Forecasting," Working Papers (Old Series) 1702, Federal Reserve Bank of Cleveland.
    13. Hanan Naser, 2015. "Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods," Empirical Economics, Springer, vol. 49(2), pages 449-479, September.
    14. Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 366-380, July.
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    17. Lima, Luiz Renato & Meng, Fanning & Godeiro, Lucas, 2020. "Quantile forecasting with mixed-frequency data," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1149-1162.
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    19. Reichlin, Lucrezia & Giannone, Domenico & Modugno, Michele & Banbura, Marta, 2012. "Now-casting and the real-time data flow," CEPR Discussion Papers 9112, C.E.P.R. Discussion Papers.
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    21. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    22. Matteo Mogliani & Anna Simoni, 2020. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Post-Print hal-03089878, HAL.
    23. Andrejs Bessonovs, 2015. "Suite of Latvia's GDP forecasting models," Working Papers 2015/01, Latvijas Banka.
    24. Monokroussos, George & Zhao, Yongchen, 2020. "Nowcasting in real time using popularity priors," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1173-1180.
    25. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    26. Michael W. McCracken & Michael T. Owyang & Tatevik Sekhposyan, 2021. "Real-Time Forecasting and Scenario Analysis Using a Large Mixed-Frequency Bayesian VAR," International Journal of Central Banking, International Journal of Central Banking, vol. 17(71), pages 1-41, December.
    27. Qian Chen & Xiang Gao & Shan Xie & Li Sun & Shuairu Tian & Shigeyuki Hamori, 2021. "On the Predictability of China Macro Indicator with Carbon Emissions Trading," Energies, MDPI, vol. 14(5), pages 1-24, February.
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    123. Bhaghoe, Sailesh & Ooft, Gavin, 2021. "Nowcasting Quarterly GDP Growth in Suriname with Factor-MIDAS and Mixed-Frequency VAR Models," Studies in Applied Economics 176, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise.
    124. Oguzhan Cepni & Rangan Gupta & Yigit Onay, 2022. "The role of investor sentiment in forecasting housing returns in China: A machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1725-1740, December.
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    131. Bjørn Eraker & Ching Wai (Jeremy) Chiu & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2015. "Bayesian Mixed Frequency VARs," Journal of Financial Econometrics, Oxford University Press, vol. 13(3), pages 698-721.
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    136. Bangwayo-Skeete, Prosper F. & Skeete, Ryan W., 2015. "Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach," Tourism Management, Elsevier, vol. 46(C), pages 454-464.
    137. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2022. "News media versus FRED‐MD for macroeconomic forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 63-81, January.
    138. Michelle T. Armesto & Kristie M. Engemann & Michael T. Owyang, 2010. "Forecasting with mixed frequencies," Review, Federal Reserve Bank of St. Louis, vol. 92(Nov), pages 521-536.
    139. Zeynalov, Ayaz, 2017. "Forecasting Tourist Arrivals in Prague: Google Econometrics," MPRA Paper 83268, University Library of Munich, Germany.
    140. Anastasiia Pankratova, 2024. "Forecasting Key Macroeconomic Indicators Using DMA and DMS Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 83(1), pages 32-52, March.
    141. Ryan T. Ball & Eric Ghysels, 2018. "Automated Earnings Forecasts: Beat Analysts or Combine and Conquer?," Management Science, INFORMS, vol. 64(10), pages 4936-4952, October.
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    144. Martin Feldkircher & Florian Huber & Josef Schreiner & Julia Woerz & Marcel Tirpak & Peter Toth, 2015. "Small-scale nowcasting models of GDP for selected CESEE countries," Working and Discussion Papers WP 4/2015, Research Department, National Bank of Slovakia.
    145. Diakonova, M. & Molina, L. & Mueller, H. & Pérez, J. J. & Rauh, C., 2024. "The Information Content of Conflict, Social Unrest and Policy Uncertainty Measures for Macroeconomic Forecasting," Cambridge Working Papers in Economics 2418, Faculty of Economics, University of Cambridge.
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    147. Claudia Foroni & Massimiliano Marcellino, 2014. "Mixed frequency structural VARs," Working Paper 2014/01, Norges Bank.
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    150. Maghyereh Aktham & Sweidan Osama & Awartani Basel, 2020. "Asymmetric Responses of Economic Growth to Daily Oil Price Changes: New Global Evidence from Mixed-data Sampling Approach," Review of Economics, De Gruyter, vol. 71(2), pages 81-99, August.
    151. Diakonova, M. & Molina, L. & Mueller, H. & Pérez, J. J. & Rauh, C., 2024. "The Information Content of Conflict, Social Unrest and Policy Uncertainty Measures for Macroeconomic Forecasting," Janeway Institute Working Papers 2413, Faculty of Economics, University of Cambridge.
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    Cited by:

    1. Henzel, Steffen R. & Mayr, Johannes, 2013. "The mechanics of VAR forecast pooling—A DSGE model based Monte Carlo study," The North American Journal of Economics and Finance, Elsevier, vol. 24(C), pages 1-24.
    2. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    3. Reichlin, Lucrezia & Giannone, Domenico & Modugno, Michele & Banbura, Marta, 2012. "Now-casting and the real-time data flow," CEPR Discussion Papers 9112, C.E.P.R. Discussion Papers.
    4. Siliverstovs Boriss & Kholodilin Konstantin A., 2012. "Assessing the Real-Time Informational Content of Macroeconomic Data Releases for Now-/Forecasting GDP: Evidence for Switzerland," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 232(4), pages 429-444, August.
    5. Claudia FORONI & Massimiliano MARCELLINO, 2012. "A Comparison of Mixed Frequency Approaches for Modelling Euro Area Macroeconomic Variables," Economics Working Papers ECO2012/07, European University Institute.
    6. Jennifer L. Castle & David F. Hendry, 2010. "Nowcasting from disaggregates in the face of location shifts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 200-214.
    7. Katja Heinisch & Rolf Scheufele, 2018. "Bottom-up or direct? Forecasting German GDP in a data-rich environment," Empirical Economics, Springer, vol. 54(2), pages 705-745, March.
    8. Drechsel, Katja & Scheufele, Rolf, 2010. "Should We Trust in Leading Indicators? Evidence from the Recent Recession," IWH Discussion Papers 10/2010, Halle Institute for Economic Research (IWH).
    9. Schumacher, Christian & Marcellino, Massimiliano & Kuzin, Vladimir, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
    10. Bušs, Ginters, 2009. "Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach," MPRA Paper 16684, University Library of Munich, Germany.
    11. Robert Lehmann & Klaus Wohlrabe, 2012. "Forecasting GDP at the Regional Level with Many Predictors," CESifo Working Paper Series 3956, CESifo.
    12. Marcus Scheiblecker, 2010. "Can the Inclusion of Calendar and Temperature Effects Improve Nowcasts and Forecasts of Construction Sector Output Based on Business Surveys?," WIFO Working Papers 374, WIFO.
    13. Klaus Wohlrabe, 2009. "Macroeconomic forecasting with mixed frequencies," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(21), pages 22-33, November.
    14. Alejo Estavillo & Gabriela Mordecki, 2023. "Nowcasting del PIB para Uruguay en base a un modelo de ecuaciones puente," Documentos de Trabajo (working papers) 23-26, Instituto de Economía - IECON.
    15. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
    16. Drechsel, Katja & Scheufele, Rolf, 2011. "The Financial Crisis from a Forecaster’s Perspective," IWH Discussion Papers 5/2011, Halle Institute for Economic Research (IWH).
    17. Abdić Ademir & Resić Emina & Abdić Adem & Rovčanin Adnan, 2020. "Nowcasting GDP of Bosnia and Herzegovina: A Comparison of Forecast Accuracy Models," South East European Journal of Economics and Business, Sciendo, vol. 15(2), pages 1-14, December.
    18. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area," Discussion Paper Series 1: Economic Studies 2009,07, Deutsche Bundesbank.
    19. Kai Carstensen & Steffen Henzel & Johannes Mayr & Klaus Wohlrabe, 2009. "IFOCAST: Methods of the Ifo short-term forecast," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(23), pages 15-28, December.
    20. Drechsel, Katja & Scheufele, Rolf, 2012. "The performance of short-term forecasts of the German economy before and during the 2008/2009 recession," International Journal of Forecasting, Elsevier, vol. 28(2), pages 428-445.
    21. Schumacher Christian, 2011. "Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 28-49, February.
    22. Kitlinski, Tobias, 2015. "With or without you: Do financial data help to forecast industrial production?," Ruhr Economic Papers 558, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    23. Lenza Michele & Warmedinger Thomas, 2011. "A Factor Model for Euro-area Short-term Inflation Analysis," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 50-62, February.
    24. Philipp an de Meulen & Martin Micheli & Torsten Schmidt, 2014. "Forecasting real estate prices in Germany: the role of consumer confidence," Journal of Property Research, Taylor & Francis Journals, vol. 31(3), pages 244-263, September.

  81. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area," Discussion Paper Series 1: Economic Studies 2009,07, Deutsche Bundesbank.

    Cited by:

    1. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2010. "Should macroeconomic forecasters use daily financial data and how?," University of Cyprus Working Papers in Economics 09-2010, University of Cyprus Department of Economics.
    2. Wenzel, Lars & Wolf, André, 2013. "Short-term forecasting with business surveys: Evidence for German IHK data at federal state level," HWWI Research Papers 140, Hamburg Institute of International Economics (HWWI).
    3. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2014. "Combining distributions of real-time forecasts: An application to U.S. growth," Research Memorandum 027, Maastricht University, Graduate School of Business and Economics (GSBE).
    4. Siliverstovs Boriss & Kholodilin Konstantin A., 2012. "Assessing the Real-Time Informational Content of Macroeconomic Data Releases for Now-/Forecasting GDP: Evidence for Switzerland," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 232(4), pages 429-444, August.
    5. Daniel Hopp, 2022. "Benchmarking Econometric and Machine Learning Methodologies in Nowcasting," Papers 2205.03318, arXiv.org.
    6. Bušs, Ginters, 2009. "Comparing forecasts of Latvia's GDP using simple seasonal ARIMA models and direct versus indirect approach," MPRA Paper 16684, University Library of Munich, Germany.
    7. Daniel Hopp, 2021. "Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)," Papers 2106.08901, arXiv.org.
    8. Daniel Hopp, 2022. "Performance of long short-term memory artificial neural networks in nowcasting during the COVID-19 crisis," Papers 2203.11872, arXiv.org.
    9. Marozzi, Armando, 2021. "The ECB's tracker: nowcasting the press conferences of the ECB," Working Paper Series 2609, European Central Bank.
    10. Ghysels, Eric, 2016. "Macroeconomics and the reality of mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 294-314.
    11. Franco, Ray John Gabriel & Mapa, Dennis S., 2014. "The Dynamics of Inflation and GDP Growth: A Mixed Frequency Model Approach," MPRA Paper 55858, University Library of Munich, Germany.
    12. Hopp Daniel, 2022. "Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)," Journal of Official Statistics, Sciendo, vol. 38(3), pages 847-873, September.
    13. Bhaghoe, Sailesh & Ooft, Gavin, 2021. "Nowcasting Quarterly GDP Growth in Suriname with Factor-MIDAS and Mixed-Frequency VAR Models," Studies in Applied Economics 176, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise.
    14. Michelle T. Armesto & Kristie M. Engemann & Michael T. Owyang, 2010. "Forecasting with mixed frequencies," Review, Federal Reserve Bank of St. Louis, vol. 92(Nov), pages 521-536.
    15. Neville Francis & Eric Ghysels & Michael T. Owyang, 2011. "The low-frequency impact of daily monetary policy shocks," Working Papers 2011-009, Federal Reserve Bank of St. Louis.
    16. Diakonova, M. & Molina, L. & Mueller, H. & Pérez, J. J. & Rauh, C., 2024. "The Information Content of Conflict, Social Unrest and Policy Uncertainty Measures for Macroeconomic Forecasting," Cambridge Working Papers in Economics 2418, Faculty of Economics, University of Cambridge.
    17. Diakonova, M. & Molina, L. & Mueller, H. & Pérez, J. J. & Rauh, C., 2024. "The Information Content of Conflict, Social Unrest and Policy Uncertainty Measures for Macroeconomic Forecasting," Janeway Institute Working Papers 2413, Faculty of Economics, University of Cambridge.
    18. Yun-Yeong Kim, 2016. "Dynamic Analyses Using VAR Model with Mixed Frequency Data through Observable Representation," Korean Economic Review, Korean Economic Association, vol. 32, pages 41-75.

  82. Beck, Guenter W. & Hubrich, Kirstin & Marcellino, Massimiliano, 2009. "On the importance of sectoral shocks for price-setting," CFS Working Paper Series 2009/32, Center for Financial Studies (CFS).

    Cited by:

    1. Carlos Carvalho & Jae Won Lee, 2011. "Sectoral Price Facts in a Sticky-Price Model," Departmental Working Papers 201133, Rutgers University, Department of Economics.
    2. Antoni Espasa & Eva Senra, 2017. "Twenty-Two Years of Inflation Assessment and Forecasting Experience at the Bulletin of EU & US Inflation and Macroeconomic Analysis," Econometrics, MDPI, vol. 5(4), pages 1-28, October.

  83. Marcellino, Massimiliano & Kapetanios, George & Carriero, Andrea, 2009. "Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models," CEPR Discussion Papers 7446, C.E.P.R. Discussion Papers.

    Cited by:

    1. Teresa Buchen & Klaus Wohlrabe, 2013. "Assessing the Macroeconomic Forecasting Performance of Boosting - Evidence for the United States, the Euro Area, and Germany," CESifo Working Paper Series 4148, CESifo.
    2. Gianluca Cubadda & Alain Hecq, 2021. "Reduced Rank Regression Models in Economics and Finance," CEIS Research Paper 525, Tor Vergata University, CEIS, revised 08 Nov 2021.
    3. Exterkate, Peter & Groenen, Patrick J.F. & Heij, Christiaan & van Dijk, Dick, 2016. "Nonlinear forecasting with many predictors using kernel ridge regression," International Journal of Forecasting, Elsevier, vol. 32(3), pages 736-753.
    4. Scott Brave & R. Andrew Butters & Alejandro Justiniano, 2016. "Forecasting Economic Activity with Mixed Frequency Bayesian VARs," Working Paper Series WP-2016-5, Federal Reserve Bank of Chicago.
    5. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
    6. Inske Pirschel & Maik H. Wolters, 2018. "Forecasting with large datasets: compressing information before, during or after the estimation?," Empirical Economics, Springer, vol. 55(2), pages 573-596, September.
    7. Cross, Jamie L. & Hou, Chenghan & Koop, Gary & Poon, Aubrey, 2023. "Large stochastic volatility in mean VARs," Journal of Econometrics, Elsevier, vol. 236(1).
    8. Marco Centoni & Gianluca Cubadda, 2015. "Common Feature Analysis of Economic Time Series: An Overview and Recent Developments," CEIS Research Paper 355, Tor Vergata University, CEIS, revised 05 Oct 2015.
    9. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    10. Gianluca Cubadda & Alain Hecq, 2022. "Dimension Reduction for High Dimensional Vector Autoregressive Models," CEIS Research Paper 534, Tor Vergata University, CEIS, revised 24 Mar 2022.
    11. Gary Koop & Dimitris Korobilis & Davide Pettenuzzo, 2016. "Bayesian Compressed Vector Autoregressions," Working Papers 103, Brandeis University, Department of Economics and International Business School.
    12. Chan, Joshua C.C. & Eisenstat, Eric & Strachan, Rodney W., 2020. "Reducing the state space dimension in a large TVP-VAR," Journal of Econometrics, Elsevier, vol. 218(1), pages 105-118.
    13. Eran Raviv & Kees E. Bouwman & Dick van Dijk, 2013. "Forecasting Day-Ahead Electricity Prices: Utilizing Hourly Prices," Tinbergen Institute Discussion Papers 13-068/III, Tinbergen Institute.
    14. Bernardini, Emmanuela & Cubadda, Gianluca, 2015. "Macroeconomic forecasting and structural analysis through regularized reduced-rank regression," International Journal of Forecasting, Elsevier, vol. 31(3), pages 682-691.
    15. Rangan Gupta & Alain Kabundi & Stephen M. Miller & Josine Uwilingiye, 2011. "Using Large Data Sets to Forecast Sectoral Employment," Working papers 2011-02, University of Connecticut, Department of Economics, revised Aug 2012.
    16. Lasha Kavtaradze & Manouchehr Mokhtari, 2018. "Factor Models And Time†Varying Parameter Framework For Forecasting Exchange Rates And Inflation: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 302-334, April.
    17. Lombardi, Marco J. & Maier, Philipp, 2011. "Forecasting economic growth in the euro area during the Great Moderation and the Great Recession," Working Paper Series 1379, European Central Bank.
    18. Bartkus Algirdas, 2016. "A New Model with Regime Switching Errors: Forecasting Gdp in Times of Great Recession," Ekonomika (Economics), Sciendo, vol. 95(2), pages 7-29, February.
    19. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2012. "Forecasting government bond yields with large Bayesian vector autoregressions," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2026-2047.
    20. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Bayesian VARs: Specification Choices and Forecast Accuracy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 46-73, January.
    21. Dimitris Korobilis & Davide Pettenuzzo, 2017. "Adaptive Hierarchical Priors for High-Dimensional Vector Autoregessions," Working Papers 115, Brandeis University, Department of Economics and International Business School.
    22. Eric Eisenstat & Joshua C.C. Chan & Rodney W. Strachan, 2018. "Reducing Dimensions in a Large TVP-VAR," Working Paper Series 43, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    23. Lombardi, Marco J. & Osbat, Chiara & Schnatz, Bernd, 2010. "Global commodity cycles and linkages a FAVAR approach," Working Paper Series 1170, European Central Bank.
    24. Panagiotelis, Anastasios & Athanasopoulos, George & Hyndman, Rob J. & Jiang, Bin & Vahid, Farshid, 2019. "Macroeconomic forecasting for Australia using a large number of predictors," International Journal of Forecasting, Elsevier, vol. 35(2), pages 616-633.
    25. Martin Feldkircher & Nico Hauzenberger, 2019. "How useful are time-varying parameter models for forecasting economic growth in CESEE?," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q1/19, pages 29-48.
    26. Marcellino, Massimiliano & Carriero, Andrea & Corsello, Francesco, 2019. "The Global Component of Inflation Volatility," CEPR Discussion Papers 13470, C.E.P.R. Discussion Papers.
    27. Gianluca Cubadda & Barbara Guardabascio, 2017. "Representation, Estimation and Forecasting of the Multivariate Index-Augmented Autoregressive Model," CEIS Research Paper 397, Tor Vergata University, CEIS, revised 13 Jul 2018.
    28. Pierdzioch, Christian & Risse, Marian & Rohloff, Sebastian, 2016. "A boosting approach to forecasting the volatility of gold-price fluctuations under flexible loss," Resources Policy, Elsevier, vol. 47(C), pages 95-107.
    29. Bin Jiang & Anastasios Panagiotelis & George Athanasopoulos & Rob Hyndman & Farshid Vahid, 2016. "Bayesian Rank Selection in Multivariate Regression," Monash Econometrics and Business Statistics Working Papers 6/16, Monash University, Department of Econometrics and Business Statistics.
    30. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2015. "Testing for Granger causality in large mixed-frequency VARs," Discussion Papers 45/2015, Deutsche Bundesbank.
    31. Dr. Gregor Bäurle & Elizabeth Steiner & Dr. Gabriel Züllig, 2018. "Forecasting the production side of GDP," Working Papers 2018-16, Swiss National Bank.
    32. Adam Nowak & Patrick Smith, 2015. "Textual Analysis in Real Estate," Working Papers 15-34, Department of Economics, West Virginia University.
    33. Prüser, Jan, 2023. "Data-based priors for vector error correction models," International Journal of Forecasting, Elsevier, vol. 39(1), pages 209-227.
    34. Dias, Gustavo Fruet & Kapetanios, George, 2018. "Estimation and forecasting in vector autoregressive moving average models for rich datasets," Journal of Econometrics, Elsevier, vol. 202(1), pages 75-91.
    35. Churm, Rohan & Joyce, Michael & Kapetanios, George & Theodoridis, Konstantinos, 2021. "Unconventional monetary policies and the macroeconomy: The impact of the UK's QE2 and funding for lending scheme," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 721-736.
    36. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2012. "Common Drifting Volatility in Large Bayesian VARs," CEPR Discussion Papers 8894, C.E.P.R. Discussion Papers.
    37. Jamie L. Cross & Chenghan Hou & Gary Koop, 2021. "Macroeconomic Forecasting with Large Stochastic Volatility in Mean VARs," Working Papers No 04/2021, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    38. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2010. "Forecasting Government Bond Yields with Large Bayesian VARs," Working Papers 662, Queen Mary University of London, School of Economics and Finance.
    39. Gianluca Cubadda & Marco Mazzali, 2023. "The Vector Error Correction Index Model: Representation, Estimation and Identification," CEIS Research Paper 556, Tor Vergata University, CEIS, revised 04 Apr 2023.
    40. Kwon, Hyuck-Shin & Bang, Doo Won & Kim, Myeong Hyeon, 2017. "Korean Housing Cycle: Implications for Risk Management (Factor-augmented VAR Approach)," KDI Journal of Economic Policy, Korea Development Institute (KDI), vol. 39(3), pages 43-62.
    41. James H. Stock & Mark W. Watson, 2012. "Generalized Shrinkage Methods for Forecasting Using Many Predictors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 481-493, June.
    42. Churm, Rohan & Joyce, Mike & Kapetanios, George & Theodoridis, Konstantinos, 2015. "Unconventional monetary policies and the macroeconomy: the impact of the United Kingdom's QE2 and Funding for Lending Scheme," Bank of England working papers 542, Bank of England.
    43. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2016. "Structural analysis with Multivariate Autoregressive Index models," Journal of Econometrics, Elsevier, vol. 192(2), pages 332-348.
    44. Gianluca Cubadda & Stefano Grassi & Barbara Guardabascio, 2024. "The Time-Varying Multivariate Autoregressive Index Model," CEIS Research Paper 571, Tor Vergata University, CEIS, revised 10 Jan 2024.
    45. Carriero, Andrea & Mumtaz, Haroon & Theophilopoulou, Angeliki, 2015. "Macroeconomic information, structural change, and the prediction of fiscal aggregates," International Journal of Forecasting, Elsevier, vol. 31(2), pages 325-348.
    46. Joshua C.C. Chan & Rodney W. Strachan, 2023. "Bayesian State Space Models In Macroeconometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 58-75, February.
    47. Justyna Wróblewska & Anna Pajor, 2019. "One-period joint forecasts of Polish inflation, unemployment and interest rate using Bayesian VEC-MSF models," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 11(1), pages 23-45, March.
    48. Matilainen, M. & Croux, C. & Nordhausen, K. & Oja, H., 2017. "Supervised dimension reduction for multivariate time series," Econometrics and Statistics, Elsevier, vol. 4(C), pages 57-69.
    49. Doo Won Bang & HyuckShin Kwon, 2022. "Policy Impact Analysis of Housing Policies Using Housing Cycles," SAGE Open, , vol. 12(3), pages 21582440221, July.
    50. Wilms, Ines & Croux, Christophe, 2016. "Forecasting using sparse cointegration," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1256-1267.
    51. Costantini, Mauro & Kunst, Robert M., 2021. "On using predictive-ability tests in the selection of time-series prediction models: A Monte Carlo evaluation," International Journal of Forecasting, Elsevier, vol. 37(2), pages 445-460.
    52. Dimitrios P. Louzis, 2019. "Steady‐state modeling and macroeconomic forecasting quality," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(2), pages 285-314, March.
    53. Pablo Guerróon‐Quintana & Molin Zhong, 2023. "Macroeconomic forecasting in times of crises," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 295-320, April.
    54. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2020. "The economic drivers of volatility and uncertainty," Temi di discussione (Economic working papers) 1285, Bank of Italy, Economic Research and International Relations Area.
    55. Pirschel, Inske & Wolters, Maik, 2014. "Forecasting German key macroeconomic variables using large dataset methods," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100587, Verein für Socialpolitik / German Economic Association.
    56. Christophe Croux & Peter Exterkate, 2011. "Sparse and Robust Factor Modelling," Tinbergen Institute Discussion Papers 11-122/4, Tinbergen Institute.
    57. Zeng, Jing, 2014. "Forecasting Aggregates with Disaggregate Variables: Does boosting help to select the most informative predictors?," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100310, Verein für Socialpolitik / German Economic Association.
    58. Jing Zeng, 2014. "Forecasting Aggregates with Disaggregate Variables: Does Boosting Help to Select the Most Relevant Predictors?," Working Paper Series of the Department of Economics, University of Konstanz 2014-20, Department of Economics, University of Konstanz.
    59. Philippe Goulet Coulombe, 2020. "Time-Varying Parameters as Ridge Regressions," Papers 2009.00401, arXiv.org, revised Apr 2023.

  84. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, 2009. "Survey Data as Coicident or Leading Indicators," Economics Working Papers ECO2009/19, European University Institute.

    Cited by:

    1. Robert Lehmann, 2016. "Economic Growth and Business Cycle Forecasting at the Regional Level," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65.
    2. Marcellino, Massimiliano & Sivec, Vasja, 2016. "Monetary, fiscal and oil shocks: Evidence based on mixed frequency structural FAVARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 335-348.
    3. Cecilia Frale & Libero Monteforte, "undated". "FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure," Working Papers 3, Department of the Treasury, Ministry of the Economy and of Finance.
    4. Deimante Teresiene & Greta Keliuotyte-Staniuleniene & Yiyi Liao & Rasa Kanapickiene & Ruihui Pu & Siyan Hu & Xiao-Guang Yue, 2021. "The Impact of the COVID-19 Pandemic on Consumer and Business Confidence Indicators," JRFM, MDPI, vol. 14(4), pages 1-23, April.
    5. Libero Monteforte & Valentina Raponi, 2019. "Short‐term forecasts of economic activity: Are fortnightly factors useful?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(3), pages 207-221, April.
    6. Marco Cacciotti & Cecilia Frale & Serena Teobaldo, 2013. "A new methodology for a quarterly measure of the output gap," Working Papers 6, Department of the Treasury, Ministry of the Economy and of Finance.
    7. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    8. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "A Data-Driven Approach to Construct Survey-Based Indicators by Means of Evolutionary Algorithms," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(1), pages 1-14, January.
    9. Robert Lehmann, 2015. "Survey-based indicators vs. hard data: What improves export forecasts in Europe?," ifo Working Paper Series 196, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    10. Raffaella Giacomini & Barbara Rossi, 2013. "Forecasting in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 17, pages 381-408, Edward Elgar Publishing.
    11. Behrens, Christoph, 2019. "Evaluating the Joint Efficiency of German Trade Forecasts. A nonparametric multivariate approach," Working Papers 9, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    12. Bisio, Laura & Moauro, Filippo, 2017. "Temporal disaggregation by dynamic regressions: recent developments in Italian quarterly national accounts," MPRA Paper 80211, University Library of Munich, Germany, revised 14 Jul 2017.
    13. Daniel Kaufmann & Rolf Scheufele, 2015. "Business tendency surveys and macroeconomic fluctuations," KOF Working papers 15-378, KOF Swiss Economic Institute, ETH Zurich.
    14. Christoph Behrens, 2019. "A Nonparametric Evaluation of the Optimality of German Export and Import Growth Forecasts under Flexible Loss," Economies, MDPI, vol. 7(3), pages 1-23, September.
    15. Bhattacharya, Rudrani & Pandey, Radhika & Veronese, Giovanni, 2011. "Tracking India Growth in Real Time," Working Papers 11/90, National Institute of Public Finance and Policy.
    16. Chrystalleni Aristidou & Kevin Lee & Kalvinder Shields, 2015. "Real-Time Data should be used in Forecasting Output Growth and Recessionary Events in the US," Discussion Papers 2015/13, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    17. Camila Figueroa S. & Michael Pedersen, 2019. "Extracting information on economic activity from business and consumer surveys in an emerging economy (Chile)," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 22(3), pages 098-131, December.
    18. Dr. Alain Galli, 2017. "Which indicators matter? Analyzing the Swiss business cycle using a large-scale mixed-frequency dynamic factor model," Working Papers 2017-08, Swiss National Bank.
    19. Ramazan Yanik & Asfia Binte Osman & Ozcan Ozturk, 2020. "Impact of manufacturing PMI on stock market index: A study on Turkey," Journal of Administrative and Business Studies, Professor Dr. Usman Raja, vol. 6(3), pages 104-108.
    20. Anna Norin, 2011. "Nowcasting of the Gross Regional Product," ERSA conference papers ersa10p768, European Regional Science Association.
    21. Barhoumi, K. & Darné, O. & Ferrara, L., 2013. "Dynamic Factor Models: A review of the Literature ," Working papers 430, Banque de France.
    22. Cecilia Frale & Stefano Grassi & Massimiliano Marcellino & Gianluigi Mazzi & Tommaso Proietti, 2013. "EuroMInd-C: a Disaggregate Monthly Indicator of Economic Activity for the Euro Area and member countries," CEIS Research Paper 287, Tor Vergata University, CEIS, revised 01 Oct 2013.
    23. Maximo Camacho & Gabriel Perez-Quiros, 2008. "Introducing the EURO-STING: Short Term INdicator of Euro Area Growth," Working Papers 0807, Banco de España.
    24. Bialowolski, Piotr & Kuszewski, Tomasz & Witkowski, Bartosz, 2015. "Bayesian averaging vs. dynamic factor models for forecasting economic aggregates with tendency survey data," Economics Discussion Papers 2015-28, Kiel Institute for the World Economy (IfW Kiel).
    25. Cecilia Frale, Serena Teobaldo, Marco Cacciotti, Alessandra Caretta, 2013. "A Quarterly Measure Of Potential Output In The New European Fiscal Framework," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 67(2), pages 181-197, April-Jun.
    26. Sieds, 2013. "Complete Volume LXVII n.2 2013," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 67(2), pages 1-197, April-Jun.
    27. Kajal Lahiri & George Monokroussos, 2011. "Nowcasting US GDP: The role of ISM Business Surveys," Discussion Papers 11-01, University at Albany, SUNY, Department of Economics.
    28. Moauro, Filippo, 2010. "A monthly indicator of employment in the euro area: real time analysis of indirect estimates," MPRA Paper 27797, University Library of Munich, Germany, revised 30 Dec 2010.
    29. Daniel Roash & Tanya Suhoy, 2019. "Sentiment Indicators Based on a Short Business Tendency Survey," Bank of Israel Working Papers 2019.11, Bank of Israel.
    30. Behrens, Christoph, 2020. "German trade forecasts since 1970: An evaluation using the panel dimension," Working Papers 26, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    31. Dr. Sandra Hanslin Grossmann & Dr. Rolf Scheufele, 2016. "Foreign PMIs: A reliable indicator for exports?," Working Papers 2016-01, Swiss National Bank.

  85. Schumacher, Christian & Marcellino, Massimiliano, 2008. "Factor-MIDAS for now- and forecasting with ragged-edge data: A model comparison for German GDP," CEPR Discussion Papers 6708, C.E.P.R. Discussion Papers.

    Cited by:

    1. Lamprou, Dimitra, 2016. "Nowcasting GDP in Greece: The impact of data revisions and forecast origin on model selection and performance," The Journal of Economic Asymmetries, Elsevier, vol. 14(PA), pages 93-102.
    2. Laurent Ferrara & Clément Marsilli, 2012. "Financial variables as leading indicators of GDP growth: Evidence from a MIDAS approach during the Great Recession," Working Papers hal-04141077, HAL.
    3. Dominique Guégan & Patrick Rakotomarolahy, 2010. "A Short Note on the Nowcasting and the Forecasting of Euro-area GDP Using Non-Parametric Techniques," Economics Bulletin, AccessEcon, vol. 30(1), pages 508-518.
    4. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    5. Bouwman, Kees E. & Jacobs, Jan P.A.M., 2005. "Forecasting with real-time macroeconomic data: the ragged-edge problem and revisions," CCSO Working Papers 200505, University of Groningen, CCSO Centre for Economic Research.
    6. Andrade, P. & Fourel, V. & Ghysels, E. & Idier, I., 2013. "The financial content of inflation risks in the euro area," Working papers 437, Banque de France.
    7. Qian Chen & Xiang Gao & Shan Xie & Li Sun & Shuairu Tian & Shigeyuki Hamori, 2021. "On the Predictability of China Macro Indicator with Carbon Emissions Trading," Energies, MDPI, vol. 14(5), pages 1-24, February.
    8. Laurent Ferrara & Dominique Guegan & Patrick Rakotomarolahy, 2010. "GDP nowcasting with ragged-edge data: a semi-parametric modeling," Post-Print halshs-00460461, HAL.
    9. Claudia FORONI & Massimiliano MARCELLINO, 2012. "A Comparison of Mixed Frequency Approaches for Modelling Euro Area Macroeconomic Variables," Economics Working Papers ECO2012/07, European University Institute.
    10. Buss, Ginters, 2010. "A note on GDP now-/forecasting with dynamic versus static factor models along a business cycle," MPRA Paper 22147, University Library of Munich, Germany.
    11. Guido Bulligan & Massimiliano Marcellino & Fabrizio Venditti, 2012. "Forecasting economic activity with higher frequency targeted predictors," Temi di discussione (Economic working papers) 847, Bank of Italy, Economic Research and International Relations Area.
    12. Banerjee, Anindya & Marcellino, Massimiliano, 2008. "Factor-augmented Error Correction Models," CEPR Discussion Papers 6707, C.E.P.R. Discussion Papers.
    13. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    14. Jennifer L. Castle & David F. Hendry, 2010. "Nowcasting from disaggregates in the face of location shifts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 200-214.
    15. Dominique Guegan & Patrick Rakotomarolahy, 2010. "A Short Note on the Nowcasting and the Forecasting of Euro-area GDP Using Non-Parametric Techniques," Post-Print halshs-00460472, HAL.
    16. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "Pooling versus model selection for nowcasting with many predictors: an application to German GDP," Discussion Paper Series 1: Economic Studies 2009,03, Deutsche Bundesbank.
    17. Schumacher, Christian & Marcellino, Massimiliano & Kuzin, Vladimir, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
    18. Massimiliano Marcellino & Mario Porqueddu & Fabrizio Venditti, 2016. "Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 118-127, January.
    19. Klaus Wohlrabe, 2009. "Macroeconomic forecasting with mixed frequencies," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(21), pages 22-33, November.
    20. Laurent Ferrara & Dominique Guégan & Patrick Rakotomarolahy, 2010. "GDP nowcasting with ragged-edge data: a semi-parametric modeling," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 186-199.
    21. Nikolay Robinzonov & Klaus Wohlrabe, 2008. "Freedom of Choice in Macroeconomic Forecasting: An Illustration with German Industrial Production and Linear Models," ifo Working Paper Series 57, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    22. Martha Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Nowcasting," Working Papers ECARES ECARES 2010-021, ULB -- Universite Libre de Bruxelles.
    23. Nikolay Robinzonov & Gerhard Tutz & Torsten Hothorn, 2012. "Boosting techniques for nonlinear time series models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(1), pages 99-122, January.
    24. Dominique Guegan & Patrick Rakotomarolahy, 2010. "A Short Note on the Nowcasting and the Forecasting of Euro-area GDP Using Non-Parametric Techniques," PSE-Ecole d'économie de Paris (Postprint) halshs-00460472, HAL.
    25. Smith Paul, 2016. "Nowcasting UK GDP during the depression," Working Papers 1606, University of Strathclyde Business School, Department of Economics.
    26. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area," Discussion Paper Series 1: Economic Studies 2009,07, Deutsche Bundesbank.
    27. Antipa, Pamfili & Barhoumi, Karim & Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Journal of Policy Modeling, Elsevier, vol. 34(6), pages 864-878.
    28. Bellégo, C. & Ferrara, L., 2009. "Forecasting Euro-area recessions using time-varying binary response models for financial," Working papers 259, Banque de France.
    29. Bhaghoe, Sailesh & Ooft, Gavin, 2021. "Nowcasting Quarterly GDP Growth in Suriname with Factor-MIDAS and Mixed-Frequency VAR Models," Studies in Applied Economics 176, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise.
    30. Schumacher Christian, 2011. "Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 28-49, February.
    31. Rafael Ravnik, 2014. "Short-Term Forecasting of GDP under Structural Changes," Working Papers 40, The Croatian National Bank, Croatia.
    32. Pami Dua, 2023. "Macroeconomic Modelling and Bayesian Methods," Springer Books, in: Pami Dua (ed.), Macroeconometric Methods, chapter 0, pages 19-37, Springer.
    33. Marcellino, Massimiliano, 2011. "Markov-switching MIDAS models," CEPR Discussion Papers 8234, C.E.P.R. Discussion Papers.
    34. Massimiliano Marcellino & Christian Schumacher, 2010. "Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 518-550, August.
    35. Мекенбаева Камила // Mekenbayeva Kamila & Karel Musil, 2017. "Система прогнозирования в Национальном Банке Казахстана: наукаст на основа опросов // Forecasting system at the National Bank of Kazakhstan: survey-based nowcasting," Working Papers #2017-1, National Bank of Kazakhstan.
    36. Laurent Ferrara & Dominique Guegan & Patrick Rakotomarolahy, 2009. "GDP nowcasting with ragged-edge data : A semi-parametric modelling," Post-Print halshs-00344839, HAL.
    37. Lenza Michele & Warmedinger Thomas, 2011. "A Factor Model for Euro-area Short-term Inflation Analysis," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 50-62, February.
    38. Chen, Pu, 2009. "A Note on Updating Forecasts When New Information Arrives between Two Periods," Economics Discussion Papers 2009-22, Kiel Institute for the World Economy (IfW Kiel).
    39. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    40. Dominique Guegan & Patrick Rakotomarolahy, 2010. "A short note on the nowcasting and the forecasting of Euro-area GDP using non-parametric techniques," Post-Print halshs-00461711, HAL.

  86. Demertzis, Maria & Viegi, Nicola & Marcellino, Massimiliano, 2008. "A Measure for Credibility: Tracking US Monetary Developments," CEPR Discussion Papers 7036, C.E.P.R. Discussion Papers.

    Cited by:

    1. Timo Henckel & Gordon Menzies & Daniel J. Zizzo, 2013. "The Great Recession and the Two Dimensions of European Central Bank Credibility," Working Paper Series 13, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    2. Issler, João Victor & Soares, Ana Flávia, 2019. "Central Bank credibility and inflation expectations: a microfounded forecasting approach," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 812, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    3. Łyziak, Tomasz & Paloviita, Maritta, 2016. "Anchoring of inflation expectations in the euro area: recent evidence based on survey data," Working Paper Series 1945, European Central Bank.
    4. Carlos Medel, 2018. "Econometric Analysis on Survey-data-based Anchoring of Inflation Expectations in Chile," Working Papers Central Bank of Chile 825, Central Bank of Chile.
    5. Warwick J McKibbin & Augustus J Panton, 2018. "Twenty-five Years of Inflation Targeting in Australia: Are There Better Alternatives for the Next Twenty-five Years?," RBA Annual Conference Volume (Discontinued), in: John Simon & Maxwell Sutton (ed.),Central Bank Frameworks: Evolution or Revolution?, Reserve Bank of Australia.
    6. José Vicente Romero & Sara Naranjo-Saldarriaga, 2022. "Weather Shocks and Inflation Expectations in Semi-Structural Models," Borradores de Economia 1218, Banco de la Republica de Colombia.
    7. Pongsak Luangaram & Yuthana Sethapramote & Chutiorn Tontivanichanon, 2015. "Inflation Expectations and Monetary Policy in Thailand," PIER Discussion Papers 3, Puey Ungphakorn Institute for Economic Research.
    8. Todd E. Clark & Troy Davig, 2008. "An empirical assessment of the relationships among inflation and short- and long-term expectations," Research Working Paper RWP 08-05, Federal Reserve Bank of Kansas City.
    9. Henckel, Timo & Menzies, Gordon D. & Moffatt, Peter & Zizzo, Daniel J., 2019. "Three dimensions of central bank credibility and inferential expectations: The Euro zone," Journal of Macroeconomics, Elsevier, vol. 60(C), pages 294-308.
    10. Łyziak, Tomasz & Mackiewicz-Łyziak, Joanna, 2020. "Does fiscal stance affect inflation expectations? Evidence for European economies," Economic Analysis and Policy, Elsevier, vol. 68(C), pages 296-310.
    11. Martin Kliem & Alexander Kriwoluzky & Samad Sarferaz, 2016. "On the Low‐Frequency Relationship Between Public Deficits and Inflation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(3), pages 566-583, April.
    12. Goy, Gavin & Hommes, Cars & Mavromatis, Kostas, 2022. "Forward guidance and the role of central bank credibility under heterogeneous beliefs," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 1240-1274.
    13. Łyziak, Tomasz & Paloviita, Maritta, 2017. "Formation of inflation expectations in turbulent times: Can ECB manage inflation expectations of professional forecasters?," Bank of Finland Research Discussion Papers 13/2017, Bank of Finland.
    14. Malikane, Christopher & Mokoka, Tshepo, 2012. "Monetary policy credibility: A Phillips curve view," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(3), pages 266-271.
    15. Serkan ÇİÇEK & Cüneyt AKAR & Eray YÜCEL, 2011. "Türkiye’de enflasyon beklentilerinin çapalanması ve güvenilirlik," Iktisat Isletme ve Finans, Bilgesel Yayincilik, vol. 26(304), pages 37-55.
    16. Libich Jan, 2011. "Inflation Nutters? Modelling the Flexibility of Inflation Targeting," The B.E. Journal of Macroeconomics, De Gruyter, vol. 11(1), pages 1-36, June.

  87. Marcellino, Massimiliano & Proietti, Tommaso & Frale, Cecilia & Mazzi, Gian Luigi, 2008. "A Monthly Indicator of the Euro Area GDP," CEPR Discussion Papers 7007, C.E.P.R. Discussion Papers.

    Cited by:

    1. Cecilia Frale & Libero Monteforte, "undated". "FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure," Working Papers 3, Department of the Treasury, Ministry of the Economy and of Finance.
    2. Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 366-380, July.
    3. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, 2009. "Survey Data as Coicident or Leading Indicators," Economics Working Papers ECO2009/19, European University Institute.
    4. Angelini, Elena & Rünstler, Gerhard & Bańbura, Marta, 2008. "Estimating and forecasting the euro area monthly national accounts from a dynamic factor model," Working Paper Series 953, European Central Bank.
    5. Cecilia Frale, "undated". "Do Surveys Help in Macroeconomic Variables Disaggregation and Estimation?," Working Papers wp2008-2, Department of the Treasury, Ministry of the Economy and of Finance.
    6. Maximo Camacho & Gabriel Perez-Quiros, 2008. "Introducing the EURO-STING: Short Term INdicator of Euro Area Growth," Working Papers 0807, Banco de España.
    7. Deeney, Peter & Cummins, Mark & Dowling, Michael & Bermingham, Adam, 2015. "Sentiment in oil markets," International Review of Financial Analysis, Elsevier, vol. 39(C), pages 179-185.
    8. Maximo Camacho & Gabriel Perez-Quiros, 2009. "Ñ-STING: España Short Term INdicator of Growth," Working Papers 0912, Banco de España.
    9. Tihana Skrinjaric, 2022. "Macroeconomic effects of systemic stress: a rolling spillover index approach," Public Sector Economics, Institute of Public Finance, vol. 46(1), pages 109-140.
    10. Cecilia Frale & Valentina Raponi, 2011. "Revisions in ocial data and forecasting," Working Papers LuissLab 1194, Dipartimento di Economia e Finanza, LUISS Guido Carli.

  88. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2008. "Forecasting with Dynamic Models using Shrinkage-based Estimation," Working Papers 635, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Dias, Gustavo Fruet & Kapetanios, George, 2018. "Estimation and forecasting in vector autoregressive moving average models for rich datasets," Journal of Econometrics, Elsevier, vol. 202(1), pages 75-91.
    2. Emrah Oral & Gazanfer Unal, 2017. "Co-movement of precious metals and forecasting using scale by scale wavelet transform," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 4(01), pages 1-21, March.

  89. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Lamprou, Dimitra, 2016. "Nowcasting GDP in Greece: The impact of data revisions and forecast origin on model selection and performance," The Journal of Economic Asymmetries, Elsevier, vol. 14(PA), pages 93-102.
    2. Andrade, P. & Fourel, V. & Ghysels, E. & Idier, I., 2013. "The financial content of inflation risks in the euro area," Working papers 437, Banque de France.
    3. Buss, Ginters, 2010. "A note on GDP now-/forecasting with dynamic versus static factor models along a business cycle," MPRA Paper 22147, University Library of Munich, Germany.
    4. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    5. Laurent Ferrara & Dominique Guégan & Patrick Rakotomarolahy, 2010. "GDP nowcasting with ragged-edge data: a semi-parametric modeling," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 186-199.
    6. Dominique Guegan & Patrick Rakotomarolahy, 2010. "A Short Note on the Nowcasting and the Forecasting of Euro-area GDP Using Non-Parametric Techniques," PSE-Ecole d'économie de Paris (Postprint) halshs-00460472, HAL.
    7. Antipa, Pamfili & Barhoumi, Karim & Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Journal of Policy Modeling, Elsevier, vol. 34(6), pages 864-878.
    8. Massimiliano Marcellino & Christian Schumacher, 2010. "Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 518-550, August.

  90. George Kapetanios & Massimiliano Marcellino, 2008. "Cross-sectional Averaging and Instrumental Variable Estimation with Many Weak Instruments," Working Papers 627, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Malikane, Christopher, 2013. "A New Keynesian Triangle Phillips Curve," MPRA Paper 43548, University Library of Munich, Germany.

  91. Oscar Jorda & Massimiliano Marcellino, 2008. "Path Forecast Evaluation," Working Papers 131, University of California, Davis, Department of Economics.

    Cited by:

    1. Matei Demetrescu & Mu-Chun Wang, 2014. "Incorporating Asymmetric Preferences into Fan Charts and Path Forecasts," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(2), pages 287-297, April.
    2. Michael W. McCracken & Joseph McGillicuddy, 2017. "An Empirical Investigation of Direct and Iterated Multistep Conditional Forecasts," Working Papers 2017-40, Federal Reserve Bank of St. Louis.
    3. Grabowski, Daniel & Staszewska-Bystrova, Anna, 2018. "Skewness-Adjusted Bootstrap Confidence Intervals and Confidence Bands for Impulse Response Functions," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181590, Verein für Socialpolitik / German Economic Association.
    4. Stefan Bruder, 2014. "Comparing several methods to compute joint prediction regions for path forecasts generated by vector autoregressions," ECON - Working Papers 181, Department of Economics - University of Zurich, revised Dec 2015.
    5. Ricardo J. Bessa & Corinna Möhrlen & Vanessa Fundel & Malte Siefert & Jethro Browell & Sebastian Haglund El Gaidi & Bri-Mathias Hodge & Umit Cali & George Kariniotakis, 2017. "Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry," Energies, MDPI, vol. 10(9), pages 1-48, September.
    6. Andrew Martinez, 2017. "Testing for Differences in Path Forecast Accuracy: Forecast-Error Dynamics Matter," Working Papers (Old Series) 1717, Federal Reserve Bank of Cleveland.
    7. Lütkepohl, Helmut & Staszewska-Bystrova, Anna & Winker, Peter, 2020. "Constructing joint confidence bands for impulse response functions of VAR models – A review," Econometrics and Statistics, Elsevier, vol. 13(C), pages 69-83.
    8. David F. Hendry & Felix Pretis, 2020. "Analyzing Differences between Scenarios," Economics Papers 2020-W05, Economics Group, Nuffield College, University of Oxford.
    9. Staszewska-Bystrova Anna, 2013. "Modified Scheffé’s Prediction Bands," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 233(5-6), pages 680-690, October.
    10. Jeanne Aslak Petersen & Ditte Heide-Jørgensen & Nina Detlefsen & Trine Boomsma, 2016. "Short-term balancing of supply and demand in an electricity system: forecasting and scheduling," Annals of Operations Research, Springer, vol. 238(1), pages 449-473, March.
    11. Kilian, Lutz & Kim, Yun Jung, 2009. "Do Local Projections Solve the Bias Problem in Impulse Response Inference?," CEPR Discussion Papers 7266, C.E.P.R. Discussion Papers.
    12. Schüssler, Rainer & Trede, Mark, 2016. "Constructing minimum-width confidence bands," Economics Letters, Elsevier, vol. 145(C), pages 182-185.
    13. Lynda Khalaf & Beatriz Peraza López, 2020. "Simultaneous Indirect Inference, Impulse Responses and ARMA Models," Econometrics, MDPI, vol. 8(2), pages 1-26, April.
    14. Christodoulakis, George, 2020. "Estimating the term structure of commodity market preferences," European Journal of Operational Research, Elsevier, vol. 282(3), pages 1146-1163.
    15. Pinto, Rui & Bessa, Ricardo J. & Matos, Manuel A., 2017. "Multi-period flexibility forecast for low voltage prosumers," Energy, Elsevier, vol. 141(C), pages 2251-2263.
    16. Farooq Akram & Andrew Binning & Junior Maih, 2016. "Joint prediction bands for macroeconomic risk management," Working Paper 2016/7, Norges Bank.
    17. Carson, Richard T. & Cenesizoglu, Tolga & Parker, Roger, 2011. "Forecasting (aggregate) demand for US commercial air travel," International Journal of Forecasting, Elsevier, vol. 27(3), pages 923-941, July.
    18. Håvard Hungnes, 2020. "Equal predictability test for multi-step-ahead system forecasts invariant to linear transformations," Discussion Papers 931, Statistics Norway, Research Department.
    19. Helmut Lütkepohl & Anna Staszewska-Bystrova & Peter Winker, 2014. "Confidence Bands for Impulse Responses: Bonferroni versus Wald," CESifo Working Paper Series 4634, CESifo.
    20. Marcellino, Massimiliano & Knüppel, Malte & Jordà , Òscar, 2010. "Empirical Simultaneous Confidence Regions for Path-Forecasts," CEPR Discussion Papers 7797, C.E.P.R. Discussion Papers.
    21. Tomasz Serafin & Grzegorz Marcjasz & Rafal Weron, 2020. "Trading on short-term path forecasts of intraday electricity prices," WORking papers in Management Science (WORMS) WORMS/20/17, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    22. Antoniadis, Anestis & Brossat, Xavier & Cugliari, Jairo & Poggi, Jean-Michel, 2016. "A prediction interval for a function-valued forecast model: Application to load forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 939-947.
    23. Charemza, Wojciech & Makarova, Svetlana & Rybiński, Krzysztof, 2022. "Economic uncertainty and natural language processing; The case of Russia," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 546-562.
    24. Olga Isengildina‐Massa & Berna Karali & Todd H. Kuethe & Ani L. Katchova, 2021. "Joint Evaluation of the System of USDA's Farm Income Forecasts," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 43(3), pages 1140-1160, September.
    25. Cepni, Oguzhan & Clements, Michael P., 2024. "How local is the local inflation factor? Evidence from emerging European countries," International Journal of Forecasting, Elsevier, vol. 40(1), pages 160-183.
    26. Giuseppe Cavaliere & Dimitris N. Politis & Anders Rahbek & Michael Wolf & Dan Wunderli, 2015. "Recent developments in bootstrap methods for dependent data," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(3), pages 352-376, May.
    27. Helmut Lütkepohl & Anna Staszewska-Bystrova & Peter Winker, 2013. "Comparison of Methods for Constructing Joint Confidence Bands for Impulse Response Functions," SFB 649 Discussion Papers SFB649DP2013-031, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    28. Dag Kolsrud, 2015. "A Time‐Simultaneous Prediction Box for a Multivariate Time Series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(8), pages 675-693, December.
    29. Chevillon, Guillaume, 2016. "Multistep forecasting in the presence of location shifts," International Journal of Forecasting, Elsevier, vol. 32(1), pages 121-137.
    30. Anna Staszewska‐Bystrova, 2011. "Bootstrap prediction bands for forecast paths from vector autoregressive models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(8), pages 721-735, December.
    31. Chevillon, Guillaume, 2009. "Multi-step forecasting in emerging economies: An investigation of the South African GDP," International Journal of Forecasting, Elsevier, vol. 25(3), pages 602-628, July.
    32. Kuethe, Todd H. & Regmi, Hari, 2023. "An Evaluation of Congressional Budget Office’s Baseline Projections of USDA Mandatory Farm and Nutrition Programs," 2023 Annual Meeting, July 23-25, Washington D.C. 335690, Agricultural and Applied Economics Association.
    33. Jeanne Aslak Petersen & Ditte Mølgård Heide-Jørgensen & Nina Kildegaard Detlefsen & Trine Krogh Boomsma, 2016. "Short-term balancing of supply and demand in an electricity system: forecasting and scheduling," Annals of Operations Research, Springer, vol. 238(1), pages 449-473, March.
    34. Staszewska-Bystrova, Anna & Winker, Peter, 2013. "Constructing narrowest pathwise bootstrap prediction bands using threshold accepting," International Journal of Forecasting, Elsevier, vol. 29(2), pages 221-233.
    35. Paolo Vidoni, 2017. "Improved multivariate prediction regions for Markov process models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 1-18, March.
    36. Òscar Jordà & Malte Knuppel & Massimiliano Marcellino, 2012. "Empirical simultaneous prediction regions for path-forecasts," Working Paper Series 2012-05, Federal Reserve Bank of San Francisco.
    37. Pinson, P. & Girard, R., 2012. "Evaluating the quality of scenarios of short-term wind power generation," Applied Energy, Elsevier, vol. 96(C), pages 12-20.

  92. Marcellino, Massimiliano & Kapetanios, George & Carriero, Andrea, 2008. "Forecasting Exchange Rates with a Large Bayesian VAR," CEPR Discussion Papers 7008, C.E.P.R. Discussion Papers.

    Cited by:

    1. Teona Shugliashvili, 2023. "The words have power: the impact of news on exchange rates," FFA Working Papers 5.006, Prague University of Economics and Business, revised 31 Jul 2023.
    2. Koop, Gary & Korobilis, Dimitris, 2016. "Model uncertainty in Panel Vector Autoregressive models," European Economic Review, Elsevier, vol. 81(C), pages 115-131.
    3. Joshua C.C. Chan & Angelia L. Grant, 2016. "Reconciling output gaps: unobserved components model and Hodrick-Prescott filter," CAMA Working Papers 2016-44, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. Huber, Florian, 2016. "Density forecasting using Bayesian global vector autoregressions with stochastic volatility," International Journal of Forecasting, Elsevier, vol. 32(3), pages 818-837.
    5. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2020. "Efficient selection of hyperparameters in large Bayesian VARs using automatic differentiation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 934-943, September.
    6. Costantini, Mauro & Crespo Cuaresma, Jesus & Hlouskova, Jaroslava, 2014. "Can Macroeconomists Get Rich Forecasting Exchange Rates?," Department of Economics Working Paper Series 176, WU Vienna University of Economics and Business.
    7. Joshua C. C. Chan & Xuewen Yu, 2022. "Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility," Papers 2206.08438, arXiv.org.
    8. Jahangoshai Rezaee, Mustafa & Jozmaleki, Mehrdad & Valipour, Mahsa, 2018. "Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 489(C), pages 78-93.
    9. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
    10. Santos, André A.P. & Nogales, Francisco J. & Ruiz, Esther & Dijk, Dick Van, 2012. "Optimal portfolios with minimum capital requirements," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 1928-1942.
    11. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian vector autoregressions," LSE Research Online Documents on Economics 87393, London School of Economics and Political Science, LSE Library.
    12. Xin Sheng & Rangan Gupta & Afees A. Salisu & Elie Bouri, 2021. "OPEC News and Exchange Rate Forecasting Using Dynamic Bayesian Learning," Working Papers 202101, University of Pretoria, Department of Economics.
    13. Alexander Chudik & M. Hashem Pesaran, 2014. "Theory and practice of GVAR modeling," Globalization Institute Working Papers 180, Federal Reserve Bank of Dallas.
    14. Gary Koop, 2012. "Using VARs and TVP-VARs with Many Macroeconomic Variables," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 4(3), pages 143-167, September.
    15. Barbara Rossi, 2013. "Exchange rate predictability," Economics Working Papers 1369, Department of Economics and Business, Universitat Pompeu Fabra.
    16. Gary Koop & Dimitris Korobilis & Davide Pettenuzzo, 2016. "Bayesian Compressed Vector Autoregressions," Working Papers 103, Brandeis University, Department of Economics and International Business School.
    17. Gary Koop & Dimitris Korobilis, 2012. "Large Time-Varying Parameter VARs," Working Paper series 11_12, Rimini Centre for Economic Analysis.
    18. Huber, Florian & Krisztin, Tamás & Piribauer, Philipp, 2014. "Forecasting Global Equity Indices Using Large Bayesian VARs," Department of Economics Working Paper Series 184, WU Vienna University of Economics and Business.
    19. Rangan Gupta & Alain Kabundi & Stephen M. Miller & Josine Uwilingiye, 2011. "Using Large Data Sets to Forecast Sectoral Employment," Working papers 2011-02, University of Connecticut, Department of Economics, revised Aug 2012.
    20. Camehl, Annika, 2023. "Penalized estimation of panel vector autoregressive models: A panel LASSO approach," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1185-1204.
    21. Joshua C. C. Chan, 2020. "Large Bayesian VARs: A Flexible Kronecker Error Covariance Structure," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 68-79, January.
    22. Joshua C. C. Chan, 2022. "Comparing Stochastic Volatility Specifications for Large Bayesian VARs," Papers 2208.13255, arXiv.org.
    23. Dovern, Jonas & Feldkircher, Martin & Huber, Florian, 2015. "Does Joint Modelling of the World Economy Pay Off? Evaluating Multivariate Forecasts from a Bayesian GVAR," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112999, Verein für Socialpolitik / German Economic Association.
    24. Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
    25. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2012. "Forecasting government bond yields with large Bayesian vector autoregressions," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2026-2047.
    26. Chihwa Kao & Lorenzo Trapani & Giovanni Urga, 2016. "Testing for Instability in Covariance Structures," Working papers 2016-33, University of Connecticut, Department of Economics.
    27. Jesus Crespo Cuaresma & Ines Fortin & Jaroslava Hlouskova, 2018. "Exchange rate forecasting and the performance of currency portfolios," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(5), pages 519-540, August.
    28. Florian Huber, 2014. "Density Forecasting using Bayesian Global Vector Autoregressions with Common Stochastic Volatility," Department of Economics Working Papers wuwp179, Vienna University of Economics and Business, Department of Economics.
    29. Papahristodoulou, Christos, 2019. "Is there any theory that explains the SEK?," MPRA Paper 95072, University Library of Munich, Germany, revised 08 Jul 2019.
    30. Florian Huber & Jesus Crespo-Cuaresma & Martin Feldkircher, 2014. "Forecasting with Bayesian Global Vector Autoregressions," ERSA conference papers ersa14p25, European Regional Science Association.
    31. Chenghan Hou & Bao Nguyen & Bo Zhang, 2023. "Real‐time forecasting of the Australian macroeconomy using flexible Bayesian VARs," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 418-451, March.
    32. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Bayesian VARs: Specification Choices and Forecast Accuracy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 46-73, January.
    33. Banerjee, Anindya & Marcellino, Massimiliano, 2008. "Factor-augmented Error Correction Models," CEPR Discussion Papers 6707, C.E.P.R. Discussion Papers.
    34. Bacchetta, Philippe & van Wincoop, Eric & Beutler, Toni, 2009. "Can Parameter Instability Explain the Meese-Rogoff Puzzle?," CEPR Discussion Papers 7383, C.E.P.R. Discussion Papers.
    35. Joshua C C Chan & Eric Eisenstat & Gary Koop, 2014. "Large Bayesian VARMAs," Working Papers 1409, University of Strathclyde Business School, Department of Economics.
    36. Sun, Shaolong & Wang, Shouyang & Wei, Yunjie, 2019. "A new multiscale decomposition ensemble approach for forecasting exchange rates," Economic Modelling, Elsevier, vol. 81(C), pages 49-58.
    37. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    38. Kim, Young Min & Lee, Seojin, 2020. "Exchange rate predictability: A variable selection perspective," International Review of Economics & Finance, Elsevier, vol. 70(C), pages 117-134.
    39. George Kapetanios & Massimiliano Marcellino & Fabrizio Venditti, 2019. "Large time‐varying parameter VARs: A nonparametric approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1027-1049, November.
    40. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2012. "Prior Selection for Vector Autoregressions," NBER Working Papers 18467, National Bureau of Economic Research, Inc.
    41. Dimitris P. Louzis, 2014. "Macroeconomic and credit forecasts in a small economy during crisis: A large Bayesian VAR approach," Working Papers 184, Bank of Greece.
    42. Simone Auer, 2014. "Monetary policy shocks and foreign investment income: evidence from a large Bayesian VAR," Globalization Institute Working Papers 170, Federal Reserve Bank of Dallas.
    43. Albis, Manuel Leonard F. & Mapa, Dennis S., 2014. "Bayesian Averaging of Classical Estimates in Asymmetric Vector Autoregressive (AVAR) Models," MPRA Paper 55902, University Library of Munich, Germany.
    44. Helmut Lütkepohl, 2014. "Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey," SFB 649 Discussion Papers SFB649DP2014-004, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    45. Joshua C. C. Chan, 2019. "Large Bayesian vector autoregressions," CAMA Working Papers 2019-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    46. Garratt, Anthony & Mise, Emi, 2014. "Forecasting exchange rates using panel model and model averaging," Economic Modelling, Elsevier, vol. 37(C), pages 32-40.
    47. Haroon Mumtaz & Nitin Kumar, 2012. "An application of data-rich environment for policy analysis of the Indian economy," Joint Research Papers 2, Centre for Central Banking Studies, Bank of England.
    48. Ahmed Ibrahim & Rasha Kashef & Menglu Li & Esteban Valencia & Eric Huang, 2020. "Bitcoin Network Mechanics: Forecasting the BTC Closing Price Using Vector Auto-Regression Models Based on Endogenous and Exogenous Feature Variables," JRFM, MDPI, vol. 13(9), pages 1-21, August.
    49. Abbate, Angela & Marcellino, Massimiliano, 2016. "Point, interval and density forecasts of exchange rates with time-varying parameter models," Discussion Papers 19/2016, Deutsche Bundesbank.
    50. Berg, Tim Oliver & Henzel, Steffen, 2013. "Point and Density Forecasts for the Euro Area Using Many Predictors: Are Large BVARs Really Superior?," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79783, Verein für Socialpolitik / German Economic Association.
    51. Ralf Brüggemann & Christian Kascha, 2017. "Directed Graphs and Variable Selection in Large Vector Autoregressive Models," Working Paper Series of the Department of Economics, University of Konstanz 2017-06, Department of Economics, University of Konstanz.
    52. Carriero, Andrea & Clements, Michael P. & Galvão, Ana Beatriz, 2015. "Forecasting with Bayesian multivariate vintage-based VARs," International Journal of Forecasting, Elsevier, vol. 31(3), pages 757-768.
    53. Schüssler, Rainer & Beckmann, Joscha & Koop, Gary & Korobilis, Dimitris, 2018. "Exchange rate predictability and dynamic Bayesian learning," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181523, Verein für Socialpolitik / German Economic Association.
    54. Joshua C. C. Chan, 2019. "Minnesota-type adaptive hierarchical priors for large Bayesian VARs," CAMA Working Papers 2019-61, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    55. Dominik Bertsche & Ralf Brüggemann & Christian Kascha, 2023. "Directed graphs and variable selection in large vector autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(2), pages 223-246, March.
    56. Jamie L. Cross & Chenghan Hou & Gary Koop, 2021. "Macroeconomic Forecasting with Large Stochastic Volatility in Mean VARs," Working Papers No 04/2021, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    57. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2010. "Forecasting Government Bond Yields with Large Bayesian VARs," Working Papers 662, Queen Mary University of London, School of Economics and Finance.
    58. Fu, Bowen, 2023. "Measuring the trend real interest rate in a data-rich environment," Journal of Economic Dynamics and Control, Elsevier, vol. 147(C).
    59. Dimitris Korobilis., 2015. "Prior selection for panel vector autoregressions," Working Papers 2015_10, Business School - Economics, University of Glasgow.
    60. Lance Kent, 2014. "Bilateral Linkages and the International Transmission of Business Cycles," Working Papers 149, Department of Economics, College of William and Mary.
    61. Reif Magnus, 2021. "Macroeconomic uncertainty and forecasting macroeconomic aggregates," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 25(2), pages 1-20, April.
    62. Jonas Dovern & Martin Feldkircher & Florian Huber, 2015. "Does Joint Modelling of the World Economy Pay Off? Evaluating Global Forecasts from a Bayesian GVAR," Working Papers 200, Oesterreichische Nationalbank (Austrian Central Bank).
    63. Kwon, Hyuck-Shin & Bang, Doo Won & Kim, Myeong Hyeon, 2017. "Korean Housing Cycle: Implications for Risk Management (Factor-augmented VAR Approach)," KDI Journal of Economic Policy, Korea Development Institute (KDI), vol. 39(3), pages 43-62.
    64. Gianluca Cubadda & Stefano Grassi & Barbara Guardabascio, 2024. "The Time-Varying Multivariate Autoregressive Index Model," CEIS Research Paper 571, Tor Vergata University, CEIS, revised 10 Jan 2024.
    65. Koop, Gary, 2014. "Forecasting with dimension switching VARs," International Journal of Forecasting, Elsevier, vol. 30(2), pages 280-290.
    66. Huber Florian, 2016. "Forecasting exchange rates using multivariate threshold models," The B.E. Journal of Macroeconomics, De Gruyter, vol. 16(1), pages 193-210, January.
    67. Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022. "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers 2212.03471, arXiv.org, revised Jul 2023.
    68. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    69. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    70. Joshua C.C. Chan & Rodney W. Strachan, 2023. "Bayesian State Space Models In Macroeconometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 58-75, February.
    71. Joshua C. C. Chan, 2022. "Asymmetric conjugate priors for large Bayesian VARs," Quantitative Economics, Econometric Society, vol. 13(3), pages 1145-1169, July.
    72. Ponomareva, Natalia & Sheen, Jeffrey & Wang, Ben Zhe, 2019. "Forecasting exchange rates using principal components," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 63(C).
    73. Ashwin Madhou & Tayushma Sewak & Imad Moosa & Vikash Ramiah, 2017. "GDP nowcasting: application and constraints in a small open developing economy," Applied Economics, Taylor & Francis Journals, vol. 49(38), pages 3880-3890, August.
    74. Ms. Adina Popescu & Ms. Alina Carare, 2011. "Monetary Policy and Risk-Premium Shocks in Hungary: Results from a Large Bayesian VAR," IMF Working Papers 2011/259, International Monetary Fund.
    75. Yemba, Boniface P. & Otunuga, Olusegun Michael & Tang, Biyan & Biswas, Nabaneeta, 2023. "Nowcasting of the Short-run Euro-Dollar Exchange Rate with Economic Fundamentals and Time-varying Parameters," Finance Research Letters, Elsevier, vol. 52(C).
    76. Doo Won Bang & HyuckShin Kwon, 2022. "Policy Impact Analysis of Housing Policies Using Housing Cycles," SAGE Open, , vol. 12(3), pages 21582440221, July.
    77. Zhang, Bo & Nguyen, Bao H., 2020. "Real-time forecasting of the Australian macroeconomy using Bayesian VARs," Working Papers 2020-12, University of Tasmania, Tasmanian School of Business and Economics.
    78. Brian D. Deaton, 2018. "Effects of the Swiss Franc/Euro Exchange Rate Floor on the Calibration of Probability Forecasts," Forecasting, MDPI, vol. 1(1), pages 1-23, May.
    79. Brahim Gaies, Khaled Guesmi, Thomas Porcher, Raphael Boroumand, 2020. "Financial instability and oil price fluctuations: evidence from oil exporting developing countries," European Journal of Comparative Economics, Cattaneo University (LIUC), vol. 17(1), pages 55-71, June.
    80. Inoue, Atsushi & Jin, Lu & Rossi, Barbara, 2017. "Rolling window selection for out-of-sample forecasting with time-varying parameters," Journal of Econometrics, Elsevier, vol. 196(1), pages 55-67.
    81. Antonio Pacifico, 2022. "Structural Compressed Panel VAR with Stochastic Volatility: A Robust Bayesian Model Averaging Procedure," Econometrics, MDPI, vol. 10(3), pages 1-24, July.
    82. Dimitrios P. Louzis, 2017. "Macroeconomic and credit forecasts during the Greek crisis using Bayesian VARs," Empirical Economics, Springer, vol. 53(2), pages 569-598, September.
    83. Haskamp, Ulrich, 2017. "Forecasting exchange rates: The time-varying relationship between exchange rates and Taylor rule fundamentals," Ruhr Economic Papers 704, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    84. David Alan Peel & Pantelis Promponas, 2016. "Forecasting the nominal exchange rate movements in a changing world. The case of the U.S. and the U.K," Working Papers 144439514, Lancaster University Management School, Economics Department.
    85. Tim Oliver Berg & Steffen Henzel, 2014. "Point and Density Forecasts for the Euro Area Using Bayesian VARs," CESifo Working Paper Series 4711, CESifo.
    86. Nguyen, BH & Zhang, Bo, 2022. "Forecasting oil Prices: can large BVARs help?," Working Papers 2022-04, University of Tasmania, Tasmanian School of Business and Economics.
    87. Valentina Aprigliano, 2020. "A large Bayesian VAR with a block‐specific shrinkage: A forecasting application for Italian industrial production," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1291-1304, December.
    88. Feldkircher, Martin & Huber, Florian, 2016. "The international transmission of US shocks—Evidence from Bayesian global vector autoregressions," European Economic Review, Elsevier, vol. 81(C), pages 167-188.
    89. Cobb, Marcus P A, 2017. "Aggregate Density Forecasting from Disaggregate Components Using Large VARs," MPRA Paper 76849, University Library of Munich, Germany.
    90. Jesús Crespo Cuaresma & Martin Feldkircher & Florian Huber, 2014. "Forecasting with Bayesian Global Vector Autoregressive Models: A Comparison of Priors," Working Papers 189, Oesterreichische Nationalbank (Austrian Central Bank).

  93. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," CEPR Discussion Papers 6706, C.E.P.R. Discussion Papers.

    Cited by:

    1. Marcellino, Massimiliano & Eickmeier, Sandra & Lemke, Wolfgang, 2011. "Classical time-varying FAVAR models - Estimation, forecasting and structural analysis," CEPR Discussion Papers 8321, C.E.P.R. Discussion Papers.
    2. Han, Xu & Inoue, Atsushi, 2015. "Tests For Parameter Instability In Dynamic Factor Models," Econometric Theory, Cambridge University Press, vol. 31(5), pages 1117-1152, October.
    3. Badi H. Baltagi & Chihwa Kao & Fa Wang, 2016. "The Identification and Estimation of a Large Factor Model with Structural Instability," Center for Policy Research Working Papers 194, Center for Policy Research, Maxwell School, Syracuse University.
    4. Alessandro Casini, 2018. "Tests for Forecast Instability and Forecast Failure under a Continuous Record Asymptotic Framework," Papers 1803.10883, arXiv.org, revised Dec 2018.
    5. Kajal Lahiri & George Monokroussos & Yongchen Zhao, 2016. "Forecasting Consumption: the Role of Consumer Confidence in Real Time with many Predictors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1254-1275, November.
    6. Niu, Linlin & Xu, Xiu & Chen, Ying, 2017. "An adaptive approach to forecasting three key macroeconomic variables for transitional China," Economic Modelling, Elsevier, vol. 66(C), pages 201-213.
    7. Nivín, Rafael & Pérez, Fernando, 2019. "Estimación de un Índice de Condiciones Financieras para el Perú," Working Papers 2019-006, Banco Central de Reserva del Perú.
    8. Boniface Yemba & Yi Duan & Nabaneeta Biswas, 2023. "Government spending news and stock price index," Economics Bulletin, AccessEcon, vol. 43(4), pages 1816-1841.
    9. Norman R. Swanson & Weiqi Xiong, 2018. "Big data analytics in economics: What have we learned so far, and where should we go from here?," Canadian Journal of Economics, Canadian Economics Association, vol. 51(3), pages 695-746, August.
    10. Anindya Banerjee & Victor Bystrov & Paul Mizen, 2010. "Interest rate pass-through in the major European economies - the role of expectations," Discussion Papers 10-07, Department of Economics, University of Birmingham.
    11. Bates, Brandon J. & Plagborg-Møller, Mikkel & Stock, James H. & Watson, Mark W., 2013. "Consistent Factor Estimation in Dynamic Factor Models with Structural Instability," Scholarly Articles 28469786, Harvard University Department of Economics.
    12. González-Rivera, Gloria & Ruiz Ortega, Esther & Maldonado, Javier, 2018. "Growth in Stress," DES - Working Papers. Statistics and Econometrics. WS 26623, Universidad Carlos III de Madrid. Departamento de Estadística.
    13. Banerjee, Anindya & Marcellino, Massimiliano, 2008. "Factor-augmented Error Correction Models," CEPR Discussion Papers 6707, C.E.P.R. Discussion Papers.
    14. Bai, Jushan & Han, Xu & Shi, Yutang, 2020. "Estimation and inference of change points in high-dimensional factor models," Journal of Econometrics, Elsevier, vol. 219(1), pages 66-100.
    15. Demetrescu, Matei & Hacıoğlu Hoke, Sinem, 2019. "Predictive regressions under asymmetric loss: Factor augmentation and model selection," International Journal of Forecasting, Elsevier, vol. 35(1), pages 80-99.
    16. Corradi, Valentina & Swanson, Norman R., 2014. "Testing for structural stability of factor augmented forecasting models," Journal of Econometrics, Elsevier, vol. 182(1), pages 100-118.
    17. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "Pooling versus model selection for nowcasting with many predictors: an application to German GDP," Discussion Paper Series 1: Economic Studies 2009,03, Deutsche Bundesbank.
    18. Germán López, 2015. "Forecast Accuracy of Small and Large Scale Dynamic Factor Models in Developing Economies," Working Papers. Serie AD 2015-03, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
    19. Breitung, Jörg & Eickmeier, Sandra, 2011. "Testing for structural breaks in dynamic factor models," Journal of Econometrics, Elsevier, vol. 163(1), pages 71-84, July.
    20. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    21. MeiChi Huang, 2019. "A Nationwide or Localized Housing Crisis? Evidence from Structural Instability in US Housing Price and Volume Cycles," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1547-1563, April.
    22. Angela Abbate & Sandra Eickmeier & Wolfgang Lemke & Massimiliano Marcellino, 2016. "The Changing International Transmission of Financial Shocks: Evidence from a Classical Time‐Varying FAVAR," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(4), pages 573-601, June.
    23. Chen, Liang & Dolado, Juan José & Gonzalo, Jesús, 2011. "Detecting big structural breaks in large factor models," UC3M Working papers. Economics we1141, Universidad Carlos III de Madrid. Departamento de Economía.
    24. Urga, Giovanni & Wang, Fa, 2022. "Estimation and inference for high dimensional factor model with regime switching," MPRA Paper 113172, University Library of Munich, Germany.
    25. Gupta, Rangan & Ma, Jun & Risse, Marian & Wohar, Mark E., 2018. "Common business cycles and volatilities in US states and MSAs: The role of economic uncertainty," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 317-337.
    26. Bueno, José Luis Cendejas & Santos, Sonia de Lucas & Rodríguez, Ma Jesús Delgado & Ayuso, Inmaculada Álvarez, 2011. "Testing for structural breaks in factor loadings: An application to international business cycle," Economic Modelling, Elsevier, vol. 28(1), pages 259-263.
    27. Raffaella Giacomini & Barbara Rossi, 2015. "Forecasting in Nonstationary Environments: What Works and What Doesn't in Reduced-Form and Structural Models," Annual Review of Economics, Annual Reviews, vol. 7(1), pages 207-229, August.
    28. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2013. "Model Selection in Equations with Many ‘Small’ Effects," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(1), pages 6-22, February.
    29. Alain Kabundi & Asithandile Mbelu, 2021. "Estimating a time-varying financial conditions index for South Africa," Empirical Economics, Springer, vol. 60(4), pages 1817-1844, April.
    30. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP," Economics Working Papers ECO2008/16, European University Institute.
    31. Baltagi, Badi H. & Feng, Qu & Kao, Chihwa, 2016. "Estimation of heterogeneous panels with structural breaks," Journal of Econometrics, Elsevier, vol. 191(1), pages 176-195.
    32. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05, Department of Economics, University of Birmingham.
    33. James H. Stock & Mark W. Watson, 2012. "Generalized Shrinkage Methods for Forecasting Using Many Predictors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 481-493, June.
    34. Johannes Tang Kristensen, 2012. "Factor-Based Forecasting in the Presence of Outliers: Are Factors Better Selected and Estimated by the Median than by The Mean?," CREATES Research Papers 2012-28, Department of Economics and Business Economics, Aarhus University.
    35. McNown, Robert & Seip, Knut Lehre, 2011. "Periods and structural breaks in US economic history 1959-2007," Journal of Policy Modeling, Elsevier, vol. 33(2), pages 169-182, March.
    36. Alessandro Casini & Pierre Perron, 2018. "Structural Breaks in Time Series," Boston University - Department of Economics - Working Papers Series WP2019-02, Boston University - Department of Economics.
    37. Fu, Zhonghao & Hong, Yongmiao & Wang, Xia, 2023. "Testing for structural changes in large dimensional factor models via discrete Fourier transform," Journal of Econometrics, Elsevier, vol. 233(1), pages 302-331.
    38. Lopez-Buenache, German, 2019. "The evolution of monetary policy effectiveness under macroeconomic instability," Economic Modelling, Elsevier, vol. 83(C), pages 221-233.
    39. Maldonado, Javier & Ruiz Ortega, Esther, 2017. "Accurate Subsampling Intervals of Principal Components Factors," DES - Working Papers. Statistics and Econometrics. WS 23974, Universidad Carlos III de Madrid. Departamento de Estadística.
    40. Olga Bondarenko, 2020. "The Missing “Cycle” Part and Other Thoughts on the Global Financial Cycle," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 250, pages 15-32.
    41. Yemba, Boniface P. & Otunuga, Olusegun Michael & Tang, Biyan & Biswas, Nabaneeta, 2023. "Nowcasting of the Short-run Euro-Dollar Exchange Rate with Economic Fundamentals and Time-varying Parameters," Finance Research Letters, Elsevier, vol. 52(C).
    42. Gary Koop & Dimitris Korobilis, 2013. "A new index of financial conditions," Working Papers 1307, University of Strathclyde Business School, Department of Economics.
    43. Mr. Luis Brandão-Marques & Mrs. Esther Perez Ruiz, 2017. "How Financial Conditions Matter Differently across Latin America," IMF Working Papers 2017/218, International Monetary Fund.
    44. Bystrov, Victor & di Salvatore, Antonietta, 2013. "Martingale approximation of eigenvalues for common factor representation," Statistics & Probability Letters, Elsevier, vol. 83(1), pages 233-237.
    45. Eraslan, Sercan & Schröder, Maximilian, 2019. "Nowcasting GDP with a large factor model space," Discussion Papers 41/2019, Deutsche Bundesbank.
    46. Fu, Zhonghao & Hong, Yongmiao & Su, Liangjun & Wang, Xia, 2023. "Specification tests for time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 235(2), pages 720-744.
    47. Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
    48. Urga, Giovanni & Wang, Fa, 2022. "Estimation and Inference for High Dimensional Factor Model with Regime Switching," MPRA Paper 117012, University Library of Munich, Germany, revised 10 Apr 2023.
    49. Norman R. Swanson & Weiqi Xiong & Xiye Yang, 2020. "Predicting interest rates using shrinkage methods, real‐time diffusion indexes, and model combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 587-613, August.
    50. Pang, Iris Ai Jao, 2010. "Forecasting Hong Kong economy using factor augmented vector autoregression," MPRA Paper 32495, University Library of Munich, Germany.
    51. Sandra Eickmeier & Wolfgang Lemke & Massimiliano Marcellino, 2015. "Classical time varying factor-augmented vector auto-regressive models—estimation, forecasting and structural analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(3), pages 493-533, June.
    52. Yanhong Feng & Dilong Xu & Pierre Failler & Tinghui Li, 2020. "Research on the Time-Varying Impact of Economic Policy Uncertainty on Crude Oil Price Fluctuation," Sustainability, MDPI, vol. 12(16), pages 1-24, August.
    53. Yoshiki Nakajima & Naoya Sueishi, 2022. "Forecasting the Japanese macroeconomy using high-dimensional data," The Japanese Economic Review, Springer, vol. 73(2), pages 299-324, April.
    54. Elsayed, Ahmed H. & Sousa, Ricardo M., 2022. "International monetary policy and cryptocurrency markets: dynamic and spillover effects," LSE Research Online Documents on Economics 115305, London School of Economics and Political Science, LSE Library.
    55. Davor Kunovac, 2007. "Factor Model Forecasting of Inflation in Croatia," Financial Theory and Practice, Institute of Public Finance, vol. 31(4), pages 371-393.
    56. Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.
    57. Ma, Chenchen & Tu, Yundong, 2023. "Group fused Lasso for large factor models with multiple structural breaks," Journal of Econometrics, Elsevier, vol. 233(1), pages 132-154.

  94. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2007. "Forecasting Large Datasets with Reduced Rank Multivariate Models," Working Papers 617, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Branimir, Jovanovic & Magdalena, Petrovska, 2010. "Forecasting Macedonian GDP: Evaluation of different models for short-term forecasting," MPRA Paper 43162, University Library of Munich, Germany.
    2. Branimir Jovanovic & Magdalena Petrovska, 2010. "Forecasting Macedonian GDP: Evaluation of different models for short-term forecasting," Working Papers 2010-02, National Bank of the Republic of North Macedonia, revised Aug 2010.
    3. Jana Eklund & George Kapetanios, 2008. "A Review of Forecasting Techniques for Large Data Sets," Working Papers 625, Queen Mary University of London, School of Economics and Finance.

  95. Andreas Beyer & Roger E. A. Farmer & Jérôme Henry & Massimiliano Marcellino, 2007. "Factor Analysis in a Model with Rational Expectations," NBER Working Papers 13404, National Bureau of Economic Research, Inc.

    Cited by:

    1. Giovanni Angelini & Luca Fanelli Fanelli, 2015. "Misspecification and Expectations Correction in New Keynesian DSGE Models," Quaderni di Dipartimento 1, Department of Statistics, University of Bologna.
    2. Scheufele, Rolf, 2008. "Evaluating the German (New Keynesian) Phillips Curve," IWH Discussion Papers 10/2008, Halle Institute for Economic Research (IWH).
    3. Gary Koop & M. Hashem Pesaran & Ron P. Smith, 2013. "On Identification of Bayesian DSGE Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 300-314, July.
    4. Dees, Stephane & Pesaran, M. Hashem & Smith, L. Vanessa & Smith, Ron P., 2008. "Identification of New Keynesian Phillips Curves from a Global Perspective," IZA Discussion Papers 3298, Institute of Labor Economics (IZA).
    5. Sophocles Mavroeidis & Mikkel Plagborg-Møller & James H. Stock, 2014. "Empirical Evidence on Inflation Expectations in the New Keynesian Phillips Curve," Journal of Economic Literature, American Economic Association, vol. 52(1), pages 124-188, March.
    6. Norkute, Milda, 2013. "Assessing the New Keynesian Phillips Curve in the Euro Area Using Disaggregate Data," Working Papers 2013:31, Lund University, Department of Economics.
    7. Milda Norkute, 2015. "Can the sectoral New Keynesian Phillips curve explain inflation dynamics in the Euro Area?," Empirical Economics, Springer, vol. 49(4), pages 1191-1216, December.
    8. Giesen, Sebastian & Scheufele, Rolf, 2016. "Effects of incorrect specification on the finite sample properties of full and limited information estimators in DSGE models," Journal of Macroeconomics, Elsevier, vol. 48(C), pages 1-18.
    9. Lanne, Markku & Luoto, Jani, 2011. "Autoregression-Based Estimation of the New Keynesian Phillips Curve," MPRA Paper 29801, University Library of Munich, Germany.
    10. Harun Mirza & Lidia Storjohann, 2014. "Making Weak Instrument Sets Stronger: Factor‐Based Estimation of Inflation Dynamics and a Monetary Policy Rule," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(4), pages 643-664, June.

  96. Angelini, Elena & Marcellino, Massimiliano, 2007. "Econometric analyses with backdated data: unified Germany and the euro area," Working Paper Series 752, European Central Bank.

    Cited by:

    1. Federica Ciocchetta & Wanda Cornacchia, 2019. "Assessing financial stability risks from the real estate market in Italy: an update," Questioni di Economia e Finanza (Occasional Papers) 493, Bank of Italy, Economic Research and International Relations Area.
    2. Sergei Aliukov & Jan Buleca, 2022. "Comparative Multidimensional Analysis of the Current State of European Economies Based on the Complex of Macroeconomic Indicators," Mathematics, MDPI, vol. 10(5), pages 1-29, March.
    3. Ralf Brüggemann & Jing Zeng, 2015. "Forecasting Euro-Area Macroeconomic Variables Using a Factor Model Approach for Backdating," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 22-39, February.
    4. Helmut Luetkepohl, 2009. "Forecasting Aggregated Time Series Variables: A Survey," Economics Working Papers ECO2009/17, European University Institute.

  97. Andrea Carriero & Massimiliano Marcellino, 2007. "A Comparison of Methods for the Construction of Composite Coincident and Leading Indexes for the UK," Working Papers 590, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Stekler, H.O., 2007. "The future of macroeconomic forecasting: Understanding the forecasting process," International Journal of Forecasting, Elsevier, vol. 23(2), pages 237-248.
    2. Edda Claus, 2011. "Seven Leading Indexes of New Zealand Employment," The Economic Record, The Economic Society of Australia, vol. 87(276), pages 76-89, March.
    3. Heilemann, Ullrich & Stekler, Herman, 2007. "Introduction to "The future of macroeconomic forecasting"," International Journal of Forecasting, Elsevier, vol. 23(2), pages 159-165.
    4. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    5. Jason Angelopoulos, 2017. "Creating and assessing composite indicators: Dynamic applications for the port industry and seaborne trade," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(1), pages 126-159, March.
    6. Andrea Carriero & Massimiliano Marcellino, 2007. "Monitoring the Economy of the Euro Area: A Comparison of Composite Coincident Indexes," Working Papers 319, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    7. Shirly Siew-Ling WONG & Chin-Hong PUAH & Shazali ABU MANSOR & Venus Khim-Sen LIEW, 2016. "Measuring Business Cycle Fluctuations: An Alternative Precursor To Economic Crises," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(4), pages 235-248.
    8. Heij, C., 2007. "Improved forecasting with leading indicators: the principal covariate index," Econometric Institute Research Papers EI 2007-23, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    9. Marcellino, Massimiliano & Proietti, Tommaso & Frale, Cecilia & Mazzi, Gian Luigi, 2008. "A Monthly Indicator of the Euro Area GDP," CEPR Discussion Papers 7007, C.E.P.R. Discussion Papers.
    10. Jason Angelopoulos & Costas I. Chlomoudis, 2017. "A Generalized Dynamic Factor Model for the U.S. Port Sector," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 67(1), pages 22-37, January-M.
    11. Javier Maldonado & Esther Ruiz, 2021. "Accurate Confidence Regions for Principal Components Factors," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(6), pages 1432-1453, December.
    12. Fichtner, Ferdinand & Rüffer, Rasmus & Schnatz, Bernd, 2009. "Leading indicators in a globalised world," Working Paper Series 1125, European Central Bank.
    13. Schnatz, Bernd & D'Agostino, Antonello, 2012. "Survey-based nowcasting of US growth: a real-time forecast comparison over more than 40 years," Working Paper Series 1455, European Central Bank.

  98. Andrea Carriero & Massimiliano Marcellino, 2007. "Monitoring the Economy of the Euro Area: A Comparison of Composite Coincident Indexes," Working Papers 319, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Marcellino, Massimiliano & Proietti, Tommaso & Frale, Cecilia & Mazzi, Gian Luigi, 2008. "A Monthly Indicator of the Euro Area GDP," CEPR Discussion Papers 7007, C.E.P.R. Discussion Papers.

  99. Andrea Carriero & Massimiliano Marcellino, 2007. "Sectoral Survey-based Confidence Indicators for Europe," Working Papers 320, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Christian Seiler, 2014. "Mode Preferences in Business Surveys: Evidence from Germany," ifo Working Paper Series 193, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    2. Nicoletta Pashourtidou & Andreas Tsiaklis, 2011. "An Analysis of Firms’ Expectations about Activity and Employment," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 5(1), pages 71-85, June.
    3. Andrea Carriero & Massimiliano Marcellino, 2007. "Monitoring the Economy of the Euro Area: A Comparison of Composite Coincident Indexes," Working Papers 319, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    4. Daniel Kaufmann & Rolf Scheufele, 2015. "Business tendency surveys and macroeconomic fluctuations," KOF Working papers 15-378, KOF Swiss Economic Institute, ETH Zurich.
    5. Camila Figueroa S. & Michael Pedersen, 2019. "Extracting information on economic activity from business and consumer surveys in an emerging economy (Chile)," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 22(3), pages 098-131, December.
    6. Willem Vanlaer & Samantha Bielen & Wim Marneffe, 2020. "Consumer Confidence and Household Saving Behaviors: A Cross-Country Empirical Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(2), pages 677-721, January.
    7. Kajal Lahiri & George Monokroussos, 2011. "Nowcasting US GDP: The role of ISM Business Surveys," Discussion Papers 11-01, University at Albany, SUNY, Department of Economics.
    8. Marcellino, Massimiliano & Proietti, Tommaso & Frale, Cecilia & Mazzi, Gian Luigi, 2008. "A Monthly Indicator of the Euro Area GDP," CEPR Discussion Papers 7007, C.E.P.R. Discussion Papers.
    9. Thomas Lux & Duc Thi Luu & Boyan Yanovski, 2020. "An analysis of systemic risk in worldwide economic sentiment indices," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 47(4), pages 909-928, November.
    10. Luu, Duc Thi & Yanovski, Boyan & Lux, Thomas, 2018. "An analysis of systematic risk in worldwide econonomic sentiment indices," Economics Working Papers 2018-03, Christian-Albrechts-University of Kiel, Department of Economics.

  100. Hubrich, Kirstin & Marcellino, Massimiliano & Beck, Günter W., 2006. "Regional inflation dynamics within and across euro area countries and a comparison with the US," Working Paper Series 681, European Central Bank.

    Cited by:

    1. Nagayasu, Jun, 2014. "Regional inflation, spatial location and the Balassa-Samuelson effect," MPRA Paper 59220, University Library of Munich, Germany.
    2. Eickmeier, Sandra, 2006. "Comovements and heterogeneity in the Comovements and heterogeneity in the dynamic factor model," Discussion Paper Series 1: Economic Studies 2006,31, Deutsche Bundesbank.
    3. Marcellino, Massimiliano & Eickmeier, Sandra & Lemke, Wolfgang, 2011. "Classical time-varying FAVAR models - Estimation, forecasting and structural analysis," CEPR Discussion Papers 8321, C.E.P.R. Discussion Papers.
    4. Zsuzsanna Zsibók & Balázs Varga, 2012. "Inflation Persistence in Hungary: a Spatial Analysis," Working Papers 1203, Department of Mathematical Economics and Economic Analysis, Corvinus University of Budapest.
    5. Andrea Vaona & Guido Ascari, 2012. "Regional Inflation Persistence: Evidence from Italy," Regional Studies, Taylor & Francis Journals, vol. 46(4), pages 509-523, June.
    6. Riccardo Cristadoro & Giuseppe Saporito & Fabrizio Venditti, 2013. "Forecasting inflation and tracking monetary policy in the euro area: does national information help?," Empirical Economics, Springer, vol. 44(3), pages 1065-1086, June.
    7. Giulio Palomba & Emma Sarno & Alberto Zazzaro, 2009. "Testing similarities of short-run inflation dynamics among EU-25 countries after the Euro," Empirical Economics, Springer, vol. 37(2), pages 231-270, October.
    8. Diego Winkelried & José Enrique Gutierrez, 2015. "Regional Inflation Dynamics and Inflation Targeting. The Case of Peru," Journal of Applied Economics, Taylor & Francis Journals, vol. 18(2), pages 199-224, November.
    9. Yanli LI, Hongfeng PENG & Hongfeng PENG, 2013. "Inflation Persistence in Nine Latin American Countries: Panel SURKSS Test with a Fourier Function," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 132-143, October.
    10. H. Marques & G. Pino & JdD Tena, 2009. "Regional inflation dynamics using space-time models," Working Paper CRENoS 200915, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    11. Angelos Liontakis & Dimitris Kremmydas, 2013. "Food Inflation in EU: Distribution Analysis and Spatial Effects," Working Papers 2013-3, Agricultural University of Athens, Department Of Agricultural Economics.
    12. Eric Girardin & Cheikh A. T. Sall, 2018. "Inflation Dynamics of Franc-Zone Countries Determinants, Co-movements and Spatial Interactions," Open Economies Review, Springer, vol. 29(2), pages 295-320, April.
    13. Hubrich, Kirstin & Marcellino, Massimiliano & Beck, Günter, 2011. "On the importance of sectoral and regional shocks for price-setting," CEPR Discussion Papers 8357, C.E.P.R. Discussion Papers.
    14. Rüffer, Rasmus & Sánchez, Marcelo & Shen, Jian-Guang, 2007. "Emerging Asia's growth and integration: how autonomous are business cycles?," Working Paper Series 715, European Central Bank.
    15. Carla Massidda & Paolo Mattana, 2008. "Regional productivity and relative prices dynamics: the case of Italy," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 42(4), pages 945-966, December.
    16. Beck, Guenter W. & Hubrich, Kirstin & Marcellino, Massimiliano, 2009. "On the importance of sectoral shocks for price-setting," CFS Working Paper Series 2009/32, Center for Financial Studies (CFS).
    17. Kai Carstensen & Jan Hagen & Oliver Hossfeld & Abelardo Salazar Neaves, 2009. "Money Demand Stability And Inflation Prediction In The Four Largest Emu Countries," Scottish Journal of Political Economy, Scottish Economic Society, vol. 56(1), pages 73-93, February.
    18. Angelos Liontakis & Christos T. Papadas, 2010. "Distribution Dynamics of Food Price Inflation Rates in EU: An Alternative Conditional Density Estimator Approach," Working Papers 2010-6, Agricultural University of Athens, Department Of Agricultural Economics.
    19. Stefano IACUS & Giuseppe PORRO, 2013. "Does European Monetary Union make inflation dynamics more uniform?," Departmental Working Papers 2013-12, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    20. Sebastian Dullien & Ulrich Fritsche, 2007. "Does the Dispersion of Unit Labor Cost Dynamics in the EMU Imply Long-run Divergence? Results from a Comparison with the United States of America and Germany," Macroeconomics and Finance Series 200702, University of Hamburg, Department of Socioeconomics.
    21. Andrzej Toroj, 2009. "Macroeconomic adjustment and heterogeneity in the euro area," NBP Working Papers 54, Narodowy Bank Polski.
    22. Mr. Serhan Cevik, 2022. "Mind the Gap: City-Level Inflation Synchronization," IMF Working Papers 2022/166, International Monetary Fund.
    23. Kapetanios, George & Marcellino, Massimiliano, 2010. "Factor-GMM estimation with large sets of possibly weak instruments," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2655-2675, November.
    24. Aleksandra Halka & Grzegorz Szafrański, 2014. "What common factors are driving inflation in CEE countries?," EcoMod2014 6977, EcoMod.
    25. Ulrich Fritsche & Vladimir Kuzin, 2007. "Unit Labor Cost Growth Differentials in the Euro Area, Germany, and the US: Lessons from PANIC and Cluster Analysis," Discussion Papers of DIW Berlin 667, DIW Berlin, German Institute for Economic Research.
    26. Giray GOZGOR, 2013. "Unemployment Persistence and Inflation Convergence: Evidence from Regions of Turkey," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 13(1), pages 55-64.
    27. Sebastian Dullien & Ulrich Fritsche, 2008. "Does the dispersion of unit labor cost dynamics in the EMU imply long-run divergence?," International Economics and Economic Policy, Springer, vol. 5(3), pages 269-295, November.
    28. Perevyshin, Yury (Перевышин, Юрий) & Sinelnikov-Murylev, Sergei Germanovich (Синельников-Мурылев, Сергей Германович) & Skrobotov, Anton (Скроботов, Антон) & Trunin, Pavel (Трунин, Павел), 2018. "Analysis of Regional Price Differentiations [Анализ Региональной Дифференциации Цен]," Published Papers 011801, Russian Presidential Academy of National Economy and Public Administration.
    29. Schnatz, Bernd, 2006. "Is reversion to PPP in euro exchange rates non-linear?," Working Paper Series 682, European Central Bank.
    30. Sandra Eickmeier, 2009. "Comovements and heterogeneity in the euro area analyzed in a non-stationary dynamic factor model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(6), pages 933-959.
    31. Jörg Breitung & Sandra Eickmeier, 2014. "Analyzing business and financial cycles using multi-level factor models," CAMA Working Papers 2014-43, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    32. Liontakis, Angelos E. & Papadas, Christos T., 2009. "Distribution Dynamics of Food Price Inflation Rates in EU: An Alternative Conditional Density Estimator Approach," 113th Seminar, September 3-6, 2009, Chania, Crete, Greece 58084, European Association of Agricultural Economists.
    33. Ailenei, Dorel & Cristescu, Amalia, 2010. "Regional Distribution of Inflationary Pressures in Romania," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 32-43, December.
    34. Giulio PALOMBA & Emma SARNO & Alberto ZAZZARO, 2007. "Testing similarities of short-run inflation dynamics among EU countries after the Euro," Working Papers 289, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    35. Uwe Blien & Hermann Gartner & Heiko Stüber & Katja Wolf, 2009. "Regional price levels and the agglomeration wage differential in western Germany," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 43(1), pages 71-88, March.
    36. Yilmazkuday, Hakan, 2009. "Inflation Targeting and Inflation Convergence within Turkey," MPRA Paper 16770, University Library of Munich, Germany.
    37. Jun Nagayasu, 2017. "Regional inflation, spatial locations and the Balassa-Samuelson effect: Evidence from Japan," Urban Studies, Urban Studies Journal Limited, vol. 54(6), pages 1482-1499, May.
    38. Martin Brown & Ralph De Haas & Vladimir Sokolov, 2013. "Regional inflation and financial dollarisation," Working Papers 163, European Bank for Reconstruction and Development, Office of the Chief Economist.

  101. Marcellino, Massimiliano & Kapetanios, George, 2006. "A Parametric Estimation Method for Dynamic Factor Models of Large Dimensions," CEPR Discussion Papers 5620, C.E.P.R. Discussion Papers.

    Cited by:

    1. GUO-FITOUSSI, Liang, 2013. "A Comparison of the Finite Sample Properties of Selection Rules of Factor Numbers in Large Datasets," MPRA Paper 50005, University Library of Munich, Germany.
    2. Eickmeier, Sandra, 2006. "Comovements and heterogeneity in the Comovements and heterogeneity in the dynamic factor model," Discussion Paper Series 1: Economic Studies 2006,31, Deutsche Bundesbank.
    3. Dominique Guégan & Patrick Rakotomarolahy, 2010. "A Short Note on the Nowcasting and the Forecasting of Euro-area GDP Using Non-Parametric Techniques," Economics Bulletin, AccessEcon, vol. 30(1), pages 508-518.
    4. Hofmann, Boris, 2009. "Do monetary indicators lead euro area inflation?," Journal of International Money and Finance, Elsevier, vol. 28(7), pages 1165-1181, November.
    5. Alain Kabundi & Rangan Gupta, 2009. "A Large Factor Model for Forecasting Macroeconomic Variables in South Africa," Working Papers 137, Economic Research Southern Africa.
    6. Bräuning, Falk & Koopman, Siem Jan, 2014. "Forecasting macroeconomic variables using collapsed dynamic factor analysis," International Journal of Forecasting, Elsevier, vol. 30(3), pages 572-584.
    7. Kholodilin Konstantin Arkadievich & Siliverstovs Boriss, 2006. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 226(3), pages 234-259, June.
    8. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Working Papers 334, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    9. Laurent Ferrara & Dominique Guegan & Patrick Rakotomarolahy, 2010. "GDP nowcasting with ragged-edge data: a semi-parametric modeling," Post-Print halshs-00460461, HAL.
    10. Sandra Eickmeier & Joerg Breitung, 2006. "Business cycle transmission from the euro area to CEECs," Computing in Economics and Finance 2006 229, Society for Computational Economics.
    11. Ali Babikir & Henry Mwambi, 2016. "Evaluating the combined forecasts of the dynamic factor model and the artificial neural network model using linear and nonlinear combining methods," Empirical Economics, Springer, vol. 51(4), pages 1541-1556, December.
    12. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    13. Anindya Banerjee & Victor Bystrov & Paul Mizen, 2010. "Interest rate pass-through in the major European economies - the role of expectations," Discussion Papers 10-07, Department of Economics, University of Birmingham.
    14. Dominique Guegan & Patrick Rakotomarolahy, 2010. "A Short Note on the Nowcasting and the Forecasting of Euro-area GDP Using Non-Parametric Techniques," Post-Print halshs-00460472, HAL.
    15. Jason Angelopoulos, 2017. "Creating and assessing composite indicators: Dynamic applications for the port industry and seaborne trade," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(1), pages 126-159, March.
    16. Anderson, Heather M. & Vahid, Farshid, 2007. "Forecasting the Volatility of Australian Stock Returns: Do Common Factors Help?," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 76-90, January.
    17. Rangan Gupta & Alain Kabundi & Stephen M. Miller, 2009. "Forecasting the US Real House Price Index: Structural and Non-Structural Models with and without Fundamentals," Working papers 2009-42, University of Connecticut, Department of Economics.
    18. Filippo Altissimo & Riccardo Cristadoro & Mario Forni & Marco Lippi & Giovanni Veronese, 2010. "New Eurocoin: Tracking Economic Growth in Real Time," The Review of Economics and Statistics, MIT Press, vol. 92(4), pages 1024-1034, November.
    19. George Kapetanios & Gonzalo Camba-Mendez, 2005. "Forecasting euro area inflation using dynamic factor measures of underlying inflation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(7), pages 491-503.
    20. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "Pooling versus model selection for nowcasting with many predictors: an application to German GDP," Discussion Paper Series 1: Economic Studies 2009,03, Deutsche Bundesbank.
    21. In Choi & Jorg Breitung, 2011. "Factor models," Working Papers 1121, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy), revised Dec 2011.
    22. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    23. Shinya Tanaka & Eiji Kurozumi, 2010. "Investigating Finite Sample Properties of Estimators for Approximate Factor Models When N Is Small," Global COE Hi-Stat Discussion Paper Series gd10-156, Institute of Economic Research, Hitotsubashi University.
    24. Mario Forni & Alessandro Giovannelli & Marco Lippi & Stefano Soccorsi, 2016. "Dynamic Factor Model with Infinite Dimensional Factor Space: Forecasting," Working Papers ECARES ECARES 2016-16, ULB -- Universite Libre de Bruxelles.
    25. Laurent Ferrara & Dominique Guégan & Patrick Rakotomarolahy, 2010. "GDP nowcasting with ragged-edge data: a semi-parametric modeling," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 186-199.
    26. Oliver Hülsewig & Johannes Mayr & Timo Wollmershäuser, 2008. "Forecasting Euro Area Real GDP: Optimal Pooling of Information," CESifo Working Paper Series 2371, CESifo.
    27. Duangnate, Kannika & Mjelde, James W., 2017. "Comparison of data-rich and small-scale data time series models generating probabilistic forecasts: An application to U.S. natural gas gross withdrawals," Energy Economics, Elsevier, vol. 65(C), pages 411-423.
    28. Mr. Francisco d Nadal De Simone & Alain N. Kabundi, 2007. "France in the Global Economy: A Structural Approximate Dynamic Factor Model Analysis," IMF Working Papers 2007/129, International Monetary Fund.
    29. Kapetanios, George & Marcellino, Massimiliano, 2010. "Factor-GMM estimation with large sets of possibly weak instruments," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2655-2675, November.
    30. Dominique Guegan & Patrick Rakotomarolahy, 2010. "Alternative methods for forecasting GDP," Post-Print halshs-00511979, HAL.
    31. Dominique Guegan & Patrick Rakotomarolahy, 2010. "A Short Note on the Nowcasting and the Forecasting of Euro-area GDP Using Non-Parametric Techniques," PSE-Ecole d'économie de Paris (Postprint) halshs-00460472, HAL.
    32. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP," Economics Working Papers ECO2008/16, European University Institute.
    33. Eliana González, 2011. "Forecasting With Many Predictors. An Empirical Comparison," Borradores de Economia 7996, Banco de la Republica.
    34. Tjeerd M. Boonman & Jan P.A.M. Jacobs & Gerard H. Kuper, 2011. "Why didn't the Global Financial Crisis hit Latin America?," CIRANO Working Papers 2011s-63, CIRANO.
    35. Tjeerd M. Boonman & Andrea E. Sanchez Urbina, 2020. "Extreme Bounds Analysis in Early Warning Systems for Currency Crises," Open Economies Review, Springer, vol. 31(2), pages 431-470, April.
    36. Luca Di Bonaventura & Mario Forni & Francesco Pattarin, 2018. "The Forecasting Performance of Dynamic Factor Models with Vintage Data," Center for Economic Research (RECent) 138, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    37. Boriss Siliverstovs & Konstantin A. Kholodilin, 2006. "On Selection of Components for a Diffusion Index Model: It's not the Size, It's How You Use It," Discussion Papers of DIW Berlin 598, DIW Berlin, German Institute for Economic Research.
    38. B. Jungbacker & S.J. Koopman & M. van Der Wel, 2011. "Maximum likelihood estimation for dynamic factor models with missing data," Post-Print hal-00828980, HAL.
    39. Schumacher Christian, 2011. "Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 28-49, February.
    40. Jason Angelopoulos & Costas I. Chlomoudis, 2017. "A Generalized Dynamic Factor Model for the U.S. Port Sector," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 67(1), pages 22-37, January-M.
    41. Choi, In, 2012. "Efficient Estimation Of Factor Models," Econometric Theory, Cambridge University Press, vol. 28(2), pages 274-308, April.
    42. In Choi, 2011. "Efficient Estimation of Nonstationary Factor Models," Working Papers 1101, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy), revised Jun 2011.
    43. Dominique Guegan & Patrick Rakotomarolahy, 2010. "Alternative methods for forecasting GDP," PSE-Ecole d'économie de Paris (Postprint) halshs-00511979, HAL.
    44. Dominique Guegan & Patrick Rakotomarolahy, 2009. "The Multivariate k-Nearest Neighbor Model for Dependent Variables : One-Sided Estimation and Forecasting," Post-Print halshs-00423871, HAL.
    45. Anindya Banerjee & Victor Bystrov & Paul Mizen, 2013. "How Do Anticipated Changes to Short-Term Market Rates Influence Banks' Retail Interest Rates? Evidence from the Four Major Euro Area Economies," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(7), pages 1375-1414, October.
    46. Massimiliano Marcellino & Christian Schumacher, 2010. "Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 518-550, August.
    47. Laurent Ferrara & Dominique Guegan & Patrick Rakotomarolahy, 2009. "GDP nowcasting with ragged-edge data : A semi-parametric modelling," Post-Print halshs-00344839, HAL.
    48. Guido Bulligan & Roberto Golinelli & Giuseppe Parigi, 2010. "Forecasting monthly industrial production in real-time: from single equations to factor-based models," Empirical Economics, Springer, vol. 39(2), pages 303-336, October.
    49. Dimitar EFTIMOSKI, 2019. "Improving Short-Term Forecasting of Macedonian GDP: Comparing the Factor Model with the Macroeconomic Structural Equation Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 32-53, June.
    50. Qin Zhang & He Ni & Hao Xu, 2023. "Forecasting models for the Chinese macroeconomy in a data‐rich environment: Evidence from large dimensional approximate factor models with mixed‐frequency data," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(1), pages 719-767, March.
    51. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    52. Dominique Guegan & Patrick Rakotomarolahy, 2010. "A short note on the nowcasting and the forecasting of Euro-area GDP using non-parametric techniques," Post-Print halshs-00461711, HAL.
    53. Bulligan, Guido & Marcellino, Massimiliano & Venditti, Fabrizio, 2015. "Forecasting economic activity with targeted predictors," International Journal of Forecasting, Elsevier, vol. 31(1), pages 188-206.
    54. Joseph, Andreas & Kalamara, Eleni & Kapetanios, George & Potjagailo, Galina & Chakraborty, Chiranjit, 2021. "Forecasting UK inflation bottom up," Bank of England working papers 915, Bank of England, revised 27 Sep 2022.
    55. Dominique Guegan & Patrick Rakotomarolahy, 2010. "Alternative methods for forecasting GDP," Post-Print halshs-00505165, HAL.
    56. B. Jungbacker & S.J. Koopman & M. van der Wel, 2009. "Dynamic Factor Analysis in The Presence of Missing Data," Tinbergen Institute Discussion Papers 09-010/4, Tinbergen Institute, revised 11 Mar 2011.

  102. Massimiliano Marcellino & George Kapetanios, 2006. "The Role of Search Frictions and Bargaining for Inflation Dynamics," Working Papers 305, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Abo-Zaid, Salem, 2013. "Optimal monetary policy and downward nominal wage rigidity in frictional labor markets," Journal of Economic Dynamics and Control, Elsevier, vol. 37(1), pages 345-364.
    2. Burkhard Heer & Alfred Maussner, 2007. "Inflation and Output Dynamics in a Model with Labor Market Search and Capital Accumulation," CESifo Working Paper Series 2036, CESifo.
    3. Richard W P Holt, 2007. "Job Reallocation, Unemployment and Hours in a New Keynesian Model," Edinburgh School of Economics Discussion Paper Series 172, Edinburgh School of Economics, University of Edinburgh.
    4. Christoffel, Kai & Costain, James & de Walque, Gregory & Kuester, Keith & Linzert, Tobias & Millard, Stephen & Pierrard, Olivier, 2009. "Inflation dynamics with labour market matching: assessing alternative specifications," Bank of England working papers 375, Bank of England.

  103. Ralf Brueggemann & Helmut Luetkepohl & Massimiliano Marcellino, 2006. "Forecasting Euro-Area Variables with German Pre-EMU Data," Economics Working Papers ECO2006/30, European University Institute.

    Cited by:

    1. Michael Ehrmann & Marcel Fratzscher & Roberto Rigobon, 2011. "Stocks, bonds, money markets and exchange rates: measuring international financial transmission," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 948-974, September.
    2. Bekiros, Stelios & Cardani, Roberta & Paccagnini, Alessia & Villa, Stefania, 2016. "Dealing with financial instability under a DSGE modeling approach with banking intermediation: A predictability analysis versus TVP-VARs," Journal of Financial Stability, Elsevier, vol. 26(C), pages 216-227.
    3. Stelios D. Bekiros & Alessia Paccagnini, 2013. "Bayesian Forecasting with a Factor-Augmented Vector Autoregressive DSGE model," Working Paper series 22_13, Rimini Centre for Economic Analysis.
    4. Bekiros Stelios & Paccagnini Alessia, 2015. "Estimating point and density forecasts for the US economy with a factor-augmented vector autoregressive DSGE model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(2), pages 107-136, April.
    5. Jing Zeng, 2015. "Combining Country-Specific Forecasts when Forecasting Euro Area Macroeconomic Aggregates," Working Paper Series of the Department of Economics, University of Konstanz 2015-11, Department of Economics, University of Konstanz.
    6. Stelios D. Bekiros & Alessia Paccagnini, 2014. "Bayesian forecasting with small and medium scale factor-augmented vector autoregressive DSGE models," Open Access publications 10197/7322, School of Economics, University College Dublin.
    7. Stelios D. Bekiros & Alessia Paccagnini, 2013. "On the predictability of time-varying VAR and DSGE models," Open Access publications 10197/7329, School of Economics, University College Dublin.
    8. Stelios D. Bekiros & Alessia Paccagnini, 2014. "Policy-oriented macroeconomic forecasting with hybrid DGSE and time-varying parameter VAR models," Working Papers 2014-426, Department of Research, Ipag Business School.
    9. Gianni Amisano & Roberta Colavecchio, 2013. "Money Growth and Inflation: evidence from a Markov Switching Bayesian VAR," Macroeconomics and Finance Series 201304, University of Hamburg, Department of Socioeconomics.
    10. Stelios D. Bekiros & Alessia Paccagnini, 2015. "Macroprudential policy and forecasting using Hybrid DSGE models with financial frictions and State space Markov-Switching TVP-VARs," Open Access publications 10197/7333, School of Economics, University College Dublin.
    11. Stelios Bekiros & Alessia Paccagnini, 2014. "Forecasting the US Economy with a Factor-Augmented Vector Autoregressive DSGE model," Working Papers 2014-183, Department of Research, Ipag Business School.
    12. Heather M. Anderson & Mardi Dungey & Denise R Osborn & Farshid Vahid, 2010. "Financial Integration and the Construction of Historical Financial Data for the Euro Area," Centre for Growth and Business Cycle Research Discussion Paper Series 152, Economics, The University of Manchester.
    13. Heather Anderson & Mardi Dungey & Denise R. Osborn & Farshid Vahid, 2007. "Constructing Historical Euro Area Data," CAMA Working Papers 2007-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    14. Amisano, Gianni & Fagan, Gabriel, 2013. "Money growth and inflation: A regime switching approach," Journal of International Money and Finance, Elsevier, vol. 33(C), pages 118-145.
    15. Ralf Brüggemann & Jing Zeng, 2015. "Forecasting Euro-Area Macroeconomic Variables Using a Factor Model Approach for Backdating," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 22-39, February.
    16. Helmut Luetkepohl, 2009. "Forecasting Aggregated Time Series Variables: A Survey," Economics Working Papers ECO2009/17, European University Institute.
    17. Jing Zeng, 2016. "Combining country-specific forecasts when forecasting Euro area macroeconomic aggregates," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 43(2), pages 415-444, May.

  104. Marcellino, Massimiliano, 2006. "A Simple Benchmark for Forecasts of Growth and Inflation," CEPR Discussion Papers 6012, C.E.P.R. Discussion Papers.

    Cited by:

    1. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Working Papers 334, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    2. Angelini, Elena & Marcellino, Massimiliano, 2007. "Econometric analyses with backdated data: unified Germany and the euro area," Working Paper Series 752, European Central Bank.
    3. Groen, Jan J.J. & Kapetanios, George & Price, Simon, 2009. "A real time evaluation of Bank of England forecasts of inflation and growth," International Journal of Forecasting, Elsevier, vol. 25(1), pages 74-80.

  105. Marcellino, Massimiliano & Kapetanios, George, 2006. "Impulse Response Functions from Structural Dynamic Factor Models: A Monte Carlo Evaluation," CEPR Discussion Papers 5621, C.E.P.R. Discussion Papers.

    Cited by:

    1. Kapetanios, George & Marcellino, Massimiliano, 2010. "Factor-GMM estimation with large sets of possibly weak instruments," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2655-2675, November.
    2. Christian Schulz, 2008. "Forecasting economic activity for Estonia : The application of dynamic principal component analyses," Bank of Estonia Working Papers 2008-02, Bank of Estonia, revised 30 Oct 2008.
    3. Christian Schulz, 2007. "Forecasting economic growth for Estonia : application of common factor methodologies," Bank of Estonia Working Papers 2007-09, Bank of Estonia, revised 04 Sep 2007.

  106. Guenter Beck & Massimiliano Marcellino, 2006. "Regional Inflation Dynamics within and across Euro Area and a Comparison with the US," Computing in Economics and Finance 2006 338, Society for Computational Economics.

    Cited by:

    1. Nagayasu, Jun, 2014. "Regional inflation, spatial location and the Balassa-Samuelson effect," MPRA Paper 59220, University Library of Munich, Germany.
    2. Eickmeier, Sandra, 2006. "Comovements and heterogeneity in the Comovements and heterogeneity in the dynamic factor model," Discussion Paper Series 1: Economic Studies 2006,31, Deutsche Bundesbank.
    3. Zsuzsanna Zsibók & Balázs Varga, 2012. "Inflation Persistence in Hungary: a Spatial Analysis," Working Papers 1203, Department of Mathematical Economics and Economic Analysis, Corvinus University of Budapest.
    4. Andrea Vaona & Guido Ascari, 2012. "Regional Inflation Persistence: Evidence from Italy," Regional Studies, Taylor & Francis Journals, vol. 46(4), pages 509-523, June.
    5. Riccardo Cristadoro & Giuseppe Saporito & Fabrizio Venditti, 2013. "Forecasting inflation and tracking monetary policy in the euro area: does national information help?," Empirical Economics, Springer, vol. 44(3), pages 1065-1086, June.
    6. Giulio Palomba & Emma Sarno & Alberto Zazzaro, 2009. "Testing similarities of short-run inflation dynamics among EU-25 countries after the Euro," Empirical Economics, Springer, vol. 37(2), pages 231-270, October.
    7. Diego Winkelried & José Enrique Gutierrez, 2015. "Regional Inflation Dynamics and Inflation Targeting. The Case of Peru," Journal of Applied Economics, Taylor & Francis Journals, vol. 18(2), pages 199-224, November.
    8. Eric Girardin & Cheikh A. T. Sall, 2018. "Inflation Dynamics of Franc-Zone Countries Determinants, Co-movements and Spatial Interactions," Open Economies Review, Springer, vol. 29(2), pages 295-320, April.
    9. Carla Massidda & Paolo Mattana, 2008. "Regional productivity and relative prices dynamics: the case of Italy," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 42(4), pages 945-966, December.
    10. Kai Carstensen & Jan Hagen & Oliver Hossfeld & Abelardo Salazar Neaves, 2009. "Money Demand Stability And Inflation Prediction In The Four Largest Emu Countries," Scottish Journal of Political Economy, Scottish Economic Society, vol. 56(1), pages 73-93, February.
    11. Angelos Liontakis & Christos T. Papadas, 2010. "Distribution Dynamics of Food Price Inflation Rates in EU: An Alternative Conditional Density Estimator Approach," Working Papers 2010-6, Agricultural University of Athens, Department Of Agricultural Economics.
    12. Stefano IACUS & Giuseppe PORRO, 2013. "Does European Monetary Union make inflation dynamics more uniform?," Departmental Working Papers 2013-12, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    13. Sebastian Dullien & Ulrich Fritsche, 2007. "Does the Dispersion of Unit Labor Cost Dynamics in the EMU Imply Long-run Divergence? Results from a Comparison with the United States of America and Germany," Macroeconomics and Finance Series 200702, University of Hamburg, Department of Socioeconomics.
    14. Andrzej Toroj, 2009. "Macroeconomic adjustment and heterogeneity in the euro area," NBP Working Papers 54, Narodowy Bank Polski.
    15. Mr. Serhan Cevik, 2022. "Mind the Gap: City-Level Inflation Synchronization," IMF Working Papers 2022/166, International Monetary Fund.
    16. Aleksandra Halka & Grzegorz Szafrański, 2014. "What common factors are driving inflation in CEE countries?," EcoMod2014 6977, EcoMod.
    17. Ulrich Fritsche & Vladimir Kuzin, 2007. "Unit Labor Cost Growth Differentials in the Euro Area, Germany, and the US: Lessons from PANIC and Cluster Analysis," Discussion Papers of DIW Berlin 667, DIW Berlin, German Institute for Economic Research.
    18. Sebastian Dullien & Ulrich Fritsche, 2008. "Does the dispersion of unit labor cost dynamics in the EMU imply long-run divergence?," International Economics and Economic Policy, Springer, vol. 5(3), pages 269-295, November.
    19. Liontakis, Angelos E. & Papadas, Christos T., 2009. "Distribution Dynamics of Food Price Inflation Rates in EU: An Alternative Conditional Density Estimator Approach," 113th Seminar, September 3-6, 2009, Chania, Crete, Greece 58084, European Association of Agricultural Economists.
    20. Ailenei, Dorel & Cristescu, Amalia, 2010. "Regional Distribution of Inflationary Pressures in Romania," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 32-43, December.
    21. Uwe Blien & Hermann Gartner & Heiko Stüber & Katja Wolf, 2009. "Regional price levels and the agglomeration wage differential in western Germany," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 43(1), pages 71-88, March.
    22. Jun Nagayasu, 2017. "Regional inflation, spatial locations and the Balassa-Samuelson effect: Evidence from Japan," Urban Studies, Urban Studies Journal Limited, vol. 54(6), pages 1482-1499, May.

  107. Marcellino, Massimiliano & Banerjee, Anindya & Masten, Igor, 2005. "Forecasting macroeconomic variables for the new member states of the European Union," Working Paper Series 482, European Central Bank.

    Cited by:

    1. Matthias Mohr, 2005. "A Trend-Cycle(-Season) Filter," Econometrics 0508004, University Library of Munich, Germany.
    2. Sandra Eickmeier & Joerg Breitung, 2006. "Business cycle transmission from the euro area to CEECs," Computing in Economics and Finance 2006 229, Society for Computational Economics.
    3. Lombardi, Marco J. & Maier, Philipp, 2011. "Forecasting economic growth in the euro area during the Great Moderation and the Great Recession," Working Paper Series 1379, European Central Bank.
    4. Lorenzo Boldrini & Eric Hillebrand, 2015. "Supervision in Factor Models Using a Large Number of Predictors," CREATES Research Papers 2015-38, Department of Economics and Business Economics, Aarhus University.
    5. Eickmeier, Sandra & Breitung, Jorg, 2006. "How synchronized are new EU member states with the euro area? Evidence from a structural factor model," Journal of Comparative Economics, Elsevier, vol. 34(3), pages 538-563, September.
    6. Ennio Cascetta & Francesca Pagliara & Andrea Papola, 2007. "Alternative approaches to trip distribution modelling: A retrospective review and suggestions for combining different approaches," Papers in Regional Science, Wiley Blackwell, vol. 86(4), pages 597-620, November.
    7. Viktors Ajevskis & Gundars Davidsons, 2008. "Dynamic Factor Models in Forecasting Latvia's Gross Domestic Product," Working Papers 2008/02, Latvijas Banka.
    8. Christian Schulz, 2008. "Forecasting economic activity for Estonia : The application of dynamic principal component analyses," Bank of Estonia Working Papers 2008-02, Bank of Estonia, revised 30 Oct 2008.
    9. Sandra Eickmeier & Tim Ng, 2009. "Forecasting national activity using lots of international predictors: an application to New Zealand," Reserve Bank of New Zealand Discussion Paper Series DP2009/04, Reserve Bank of New Zealand.
    10. Johannes Tang Kristensen, 2012. "Factor-Based Forecasting in the Presence of Outliers: Are Factors Better Selected and Estimated by the Median than by The Mean?," CREATES Research Papers 2012-28, Department of Economics and Business Economics, Aarhus University.
    11. Sima Siami-Namini & Akbar Siami Namin, 2018. "Forecasting Economics and Financial Time Series: ARIMA vs. LSTM," Papers 1803.06386, arXiv.org.
    12. Christian Dreger & Konstantin A. Kholodilin, 2006. "Prognosen der regionalen Konjunkturentwicklung," DIW Wochenbericht, DIW Berlin, German Institute for Economic Research, vol. 73(34), pages 469-474.
    13. Christian Gillitzer & Jonathan Kearns, 2007. "Forecasting with Factors: The Accuracy of Timeliness," RBA Research Discussion Papers rdp2007-03, Reserve Bank of Australia.

  108. Favero, Carlo A. & Marcellino, Massimiliano, 2005. "Modelling and Forecasting Fiscal Variables for the euro Area," CEPR Discussion Papers 5294, C.E.P.R. Discussion Papers.

    Cited by:

    1. Philippe Burger & Estian Calitz, 2019. "Sustainable fiscal policy and economic growth in South Africa," Working Papers 15/2019, Stellenbosch University, Department of Economics.
    2. Leal, Teresa & Pérez, Javier J. & Tujula, Mika & Vidal, Jean-Pierre, 2007. "Fiscal forecasting: lessons from the literature and challenges," Working Paper Series 843, European Central Bank.
    3. Henzel, Steffen R. & Mayr, Johannes, 2013. "The mechanics of VAR forecast pooling—A DSGE model based Monte Carlo study," The North American Journal of Economics and Finance, Elsevier, vol. 24(C), pages 1-24.
    4. Paolo Casadio & Antonio Paradiso & B. Bhaskara Rao, 2012. "The dynamics of Italian public debt: alternative paths for fiscal consolidation," Applied Economics Letters, Taylor & Francis Journals, vol. 19(7), pages 635-639, May.
    5. Kumar, Saten & Paradiso, Antonio, 2011. "Assessing Sustainability of the Irish Public Debt," MPRA Paper 35295, University Library of Munich, Germany.
    6. Massimiliano Marcellino, 2008. "A linear benchmark for forecasting GDP growth and inflation?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 305-340.
    7. Andrejs Bessonovs, 2015. "Suite of Latvia's GDP forecasting models," Working Papers 2015/01, Latvijas Banka.
    8. Artem Vdovychenko, 2017. "Fiscal Policy Reaction Function and Sustainability of Fiscal Policy in Ukraine," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 240, pages 22-35.
    9. Todd E. Clark & Michael W. McCracken, 2010. "Averaging forecasts from VARs with uncertain instabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 5-29.
    10. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    11. Todd E. Clark & Michael W. McCracken, 2006. "Forecasting of small macroeconomic VARs in the presence of instabilities," Research Working Paper RWP 06-09, Federal Reserve Bank of Kansas City.
    12. Nicholas Apergis & Arusha Cooray, 2013. "Forecasting fiscal variables: Only a strong growth plan can sustain the Greek austerity programs - Evidence from simultaneous and structural models," CAMA Working Papers 2013-25, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    13. Kirdan Lees & Troy Matheson, 2005. "Mind your Ps and Qs! Improving ARMA forecasts with RBC priors," Reserve Bank of New Zealand Discussion Paper Series DP2005/02, Reserve Bank of New Zealand.
    14. Fildes, Robert & Wei, Yingqi & Ismail, Suzilah, 2011. "Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures," International Journal of Forecasting, Elsevier, vol. 27(3), pages 902-922.
    15. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
    16. Cizkowicz, Piotr & Rzonca, Andrzej & Trzeciakowski, Rafal, 2015. "Membership in the Euro area and fiscal sustainability. Analysis through panel fiscal reaction functions," MPRA Paper 61560, University Library of Munich, Germany.
    17. R. Golinelli & I. Mammi & A. Musolesi, 2018. "Parameter heterogeneity, persistence and cross-sectional dependence: new insights on fiscal policy reaction functions for the Euro area," Working Papers wp1120, Dipartimento Scienze Economiche, Universita' di Bologna.
    18. Paredes, Joan & Pedregal, Diego J. & Pérez, Javier J., 2014. "Fiscal policy analysis in the euro area: Expanding the toolkit," Journal of Policy Modeling, Elsevier, vol. 36(5), pages 800-823.
    19. Christoph Peatz, 2020. "Fiscal Rules in Good Times and Bad," IMK Working Paper 206-2020, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    20. Philippe Burger & Krige Siebrits & Estian Calitz, 2015. "The public sector balance sheet and fiscal consolidation in South Africa," Working Papers 11/2015, Stellenbosch University, Department of Economics.
    21. Sabaj, Ernil & Kahveci, Mustafa, 2018. "Forecasting tax revenues in an emerging economy: The case of Albania," MPRA Paper 84404, University Library of Munich, Germany.
    22. Esposito, Piero & Paradiso, Antonio & Rao, B. Bhaskara, 2011. "The dynamics of Spanish public debt and sustainable paths for fiscal consolidation," MPRA Paper 32563, University Library of Munich, Germany.
    23. Thomas A. Alexopoulos & Henry Thompson, 2021. "A macroeconomic simulation for Greece in the wake of its government debt crisis," Economic Change and Restructuring, Springer, vol. 54(3), pages 699-716, August.
    24. Adam Pigoń & Michał Ramsza, 2022. "A Comparison of German, Swiss, and Polish Fiscal Rules Using Monte Carlo Simulations," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 4, pages 17-41.
    25. Carabotta, Laura & Paluzie, Elisenda & Ramos, Raul, 2017. "Does fiscal responsibility matter? Evidence from public and private forecasters in Italy," International Journal of Forecasting, Elsevier, vol. 33(3), pages 694-706.
    26. Hasko, Harri, 2007. "Some unpleasant fiscal arithmetic: the role of monetary and fiscal policy in public debt dynamics since the 1970s," Bank of Finland Research Discussion Papers 28/2007, Bank of Finland.
    27. Carriero, Andrea & Mumtaz, Haroon & Theophilopoulou, Angeliki, 2015. "Macroeconomic information, structural change, and the prediction of fiscal aggregates," International Journal of Forecasting, Elsevier, vol. 31(2), pages 325-348.
    28. Marcellino, Massimiliano, 2006. "A Simple Benchmark for Forecasts of Growth and Inflation," CEPR Discussion Papers 6012, C.E.P.R. Discussion Papers.
    29. Thomas Flavin & Ekaterini Panopoulou & Theologos Pantelidis, 2009. "Forecasting growth and inflation in an enlarged euro area," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(5), pages 405-425.
    30. Vdovychenko Artem, 2016. "Fiscal Policy Rection and Sustainability of Fiscal Policy in Ukraine," EERC Working Paper Series 16/07e, EERC Research Network, Russia and CIS.
    31. Esposito, Piero & Paradiso, Antonio & Rao, B. Bhaskara, 2011. "The dynamics of French public debt: Paths for fiscal consolidations," MPRA Paper 32564, University Library of Munich, Germany.
    32. Checherita-Westphal, Cristina & Žďárek, Václav, 2017. "Fiscal reaction function and fiscal fatigue: evidence for the euro area," Working Paper Series 2036, European Central Bank.
    33. Steffen Henzel & Johannes Mayr, 2009. "The Virtues of VAR Forecast Pooling – A DSGE Model Based Monte Carlo Study," ifo Working Paper Series 65, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    34. Philippe Burger & Krige Siebrits & Estian Calitz, 2016. "Fiscal Consolidation and the Public Sector Balance Sheet in South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 84(4), pages 501-519, December.

  109. Marcellino, Massimiliano, 2005. "Leading Indicators: What Have We Learned?," CEPR Discussion Papers 4977, C.E.P.R. Discussion Papers.

    Cited by:

    1. Carriero, Andrea & Marcellino, Massimiliano, 2007. "A comparison of methods for the construction of composite coincident and leading indexes for the UK," International Journal of Forecasting, Elsevier, vol. 23(2), pages 219-236.
    2. Curran, Declan & Funke, Michael, 2006. "Taking the temperature: forecasting GDP growth for mainland in China," BOFIT Discussion Papers 6/2006, Bank of Finland Institute for Emerging Economies (BOFIT).
    3. John G Powell & Sirimon Treepongkaruna, 2012. "Recession fears as self-fulfilling prophecies? Influence on stock returns and output," Australian Journal of Management, Australian School of Business, vol. 37(2), pages 231-260, August.
    4. Maria Antoinette Silgoner, 2005. "An Overview of European Economic Indicators: Great Variety of Data on the Euro Area, Need for More Extensive Coverage of the New EU Member States," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue 3, pages 66-89.
    5. Vladimir Dubrovskiy & Inna Golodniuk & Janusz Szyrmer, 2009. "Composite Leading Indicators for Ukraine: An Early Warning Model," CASE Network Reports 0085, CASE-Center for Social and Economic Research.
    6. Idrovo Aguirre, Byron, 2007. "Los Ciclos del Mercado Inmobiliario y su Relación con los Ciclos de la Economía [Housing Market Fluctuations and the Economic Cycles]," MPRA Paper 19365, University Library of Munich, Germany, revised 24 Sep 2007.
    7. Muriel Nguiffo-Boyom, 2008. "A monthly indicator of Economic activity for Luxembourg," BCL working papers 31, Central Bank of Luxembourg.
    8. Olivier Bandt & Catherine Bruneau & Alexis Flageollet, 2006. "Assessing Aggregate Comovements in France, Germany and Italy Using a Non Stationary Factor Model of the Euro Area," Springer Books, in: Convergence or Divergence in Europe?, pages 95-120, Springer.
    9. Sylvia Kaufmann, 2008. "Dating and forecasting turning points by Bayesian clustering with dynamic structure: A suggestion with an application to Austrian data," Working Papers 144, Oesterreichische Nationalbank (Austrian Central Bank).
    10. Allen, P. Geoffrey & Morzuch, Bernard J., 2006. "Twenty-five years of progress, problems, and conflicting evidence in econometric forecasting. What about the next 25 years?," International Journal of Forecasting, Elsevier, vol. 22(3), pages 475-492.
    11. Mohsin S. Khan & Axel Schimmelpfennig, 2006. "Inflation in Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 45(2), pages 185-202.
    12. Timmermann, Allan & Aiolfi, Marco & Catão, Luís, 2010. "Common Factors in Latin America?s Business Cycles," CEPR Discussion Papers 7671, C.E.P.R. Discussion Papers.
    13. Matta Samer, 2015. "New Coincident and Leading Indexes for the Lebanese Economy," Review of Middle East Economics and Finance, De Gruyter, vol. 11(3), pages 277-303, December.
    14. Croce, Roberto M. & Haurin, Donald R., 2009. "Predicting turning points in the housing market," Journal of Housing Economics, Elsevier, vol. 18(4), pages 281-293, December.

  110. Farmer, Roger & Henry, Jerome & Marcellino, Massimiliano & Beyer, Andreas, 2005. "Factor Analysis in a New-Keynesian Model," CEPR Discussion Papers 5266, C.E.P.R. Discussion Papers.

    Cited by:

    1. Jian Gao & Gang Gong & Xue-Zhong He, 2007. "Monetary Policy and Exchange Rate Regime: Proposal for a Small and Less Developed Economy," Research Paper Series 199, Quantitative Finance Research Centre, University of Technology, Sydney.
    2. Renée B Adams & Roman Kräussl & Marco Navone & Patrick Verwijmeren & Stijn Van Nieuwerburgh, 2021. "Gendered Prices [Can culture affect prices? A cross-cultural study of shopping and retail prices]," The Review of Financial Studies, Society for Financial Studies, vol. 34(8), pages 3789-3839.
      • Renée B Adams & Roman Kräussl & Marco Navone & Patrick Verwijmeren, 2021. "Gendered Prices," Published Paper Series 2021-4, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    3. David Parsley & Helen Popper, 2009. "Evaluating Exchange Rate Management An Application to Korea," Working Papers 282009, Hong Kong Institute for Monetary Research.
    4. Andrea Carriero, 2007. "A Simple Test of the New Keynesian Phillips Curve," Working Papers 592, Queen Mary University of London, School of Economics and Finance.
    5. Andreas Beyer & Roger E. A. Farmer & Jérôme Henry & Massimiliano Marcellino, 2007. "Factor Analysis in a Model with Rational Expectations," NBER Working Papers 13404, National Bureau of Economic Research, Inc.
    6. Kapetanios, George & Marcellino, Massimiliano, 2010. "Factor-GMM estimation with large sets of possibly weak instruments," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2655-2675, November.
    7. Kapetanios, George & Marcellino, Massimiliano, 2010. "Cross-sectional averaging and instrumental variable estimation with many weak instruments," Economics Letters, Elsevier, vol. 108(1), pages 36-39, July.
    8. Andreas Beyer & Roger E.A. Farmer, 2005. "Measuring the Effects of Real and Monetary Shocks in a Structural New-Keynesian Model," Computing in Economics and Finance 2005 172, Society for Computational Economics.
    9. Beyer, Andreas & Farmer, Roger E. A., 2006. "A method to generate structural impulse-responses for measuring the effects of shocks in structural macro models," Working Paper Series 586, European Central Bank.

  111. Marcellino, Massimiliano, 2005. "Pooling-based data interpolation and backdating," CEPR Discussion Papers 5295, C.E.P.R. Discussion Papers.

    Cited by:

    1. Foroni, Claudia & Marcellino, Massimiliano & Schumacher, Christian, 2011. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," Discussion Paper Series 1: Economic Studies 2011,35, Deutsche Bundesbank.
    2. Mateusz Pipień & Sylwia Roszkowska, 2015. "Szacunki kwartalnego PKB w polskich województwach," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 5, pages 145-169.
    3. Mateusz Pipień & Sylwia Roszkowska, 2015. "Quarterly estimates of regional GDP in Poland – application of statistical inference of functions of parameters," NBP Working Papers 219, Narodowy Bank Polski.
    4. Hauber, Philipp & Schumacher, Christian, 2021. "Precision-based sampling with missing observations: A factor model application," Discussion Papers 11/2021, Deutsche Bundesbank.
    5. Schumacher, Christian & Breitung, Jörg, 2008. "Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data," International Journal of Forecasting, Elsevier, vol. 24(3), pages 386-398.

  112. Stock, James & Watson, Mark & Marcellino, Massimiliano, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," CEPR Discussion Papers 4976, C.E.P.R. Discussion Papers.

    Cited by:

    1. Christian Hutter & Enzo Weber, 2015. "Constructing a new leading indicator for unemployment from a survey among German employment agencies," Applied Economics, Taylor & Francis Journals, vol. 47(33), pages 3540-3558, July.
    2. Eric Ghysels & Leonardo Iania & Jonas Striaukas, 2018. "Quantile-based Inflation Risk Models," Working Paper Research 349, National Bank of Belgium.
    3. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    4. Leonardo Nogueira Ferreira & Silvia Miranda-Agrippino & Giovanni Ricco, 2023. "Bayesian Local Projections," Working Papers Series 581, Central Bank of Brazil, Research Department.
    5. Gachoki Emilio Munene, 2023. "Foreign Direct Investment, Trade Openness and Economic Growth in Kenya: Empirical Analysis Using ARDL Approach," International Journal of Science and Business, IJSAB International, vol. 28(1), pages 115-126.
    6. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    7. Fornaro, Paolo, 2015. "Forecasting U.S. Recessions with a Large Set of Predictors," MPRA Paper 62973, University Library of Munich, Germany.
    8. Gary Koop, 2011. "Forecasting with Medium and Large Bayesian VARs," Working Papers 1117, University of Strathclyde Business School, Department of Economics.
    9. Medel, Carlos A., 2017. "Forecasting Chilean Inflation with the Hybrid New Keynesian Phillips Curve: Globalisation, Combination, and Accuracy," MPRA Paper 78439, University Library of Munich, Germany.
    10. Yuri S. Popkov & Alexey Yu. Popkov & Yuri A. Dubnov & Dimitri Solomatine, 2020. "Entropy-Randomized Forecasting of Stochastic Dynamic Regression Models," Mathematics, MDPI, vol. 8(7), pages 1-20, July.
    11. Edith Skriner, 2008. "Forecasting Global Flows," FIW Working Paper series 009, FIW.
    12. Klaus Abberger & Michael Graff & Boriss Siliverstovs & Jan-Egbert Sturm, 2014. "The KOF Economic Barometer, Version 2014," KOF Working papers 14-353, KOF Swiss Economic Institute, ETH Zurich.
    13. Wooi Chen Khoo & Seng Huat Ong & Biswas Atanu, 2022. "Coherent Forecasting for a Mixed Integer-Valued Time Series Model," Mathematics, MDPI, vol. 10(16), pages 1-15, August.
    14. Edda Claus, 2011. "Seven Leading Indexes of New Zealand Employment," The Economic Record, The Economic Society of Australia, vol. 87(276), pages 76-89, March.
    15. Jed Armstrong, 2015. "The Reserve Bank of New Zealand’s output gap indicator suite and its real-time properties," Reserve Bank of New Zealand Analytical Notes series AN2015/08, Reserve Bank of New Zealand.
    16. Laura Liu, 2020. "Density Forecasts in Panel Data Models: A Semiparametric Bayesian Perspective," CAEPR Working Papers 2020-003, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    17. Pincheira, Pablo M. & West, Kenneth D., 2016. "A comparison of some out-of-sample tests of predictability in iterated multi-step-ahead forecasts," Research in Economics, Elsevier, vol. 70(2), pages 304-319.
    18. Tim Bollerslev & Andrew J. Patton & Rogier Quaedvlieg, 2016. "Modeling and Forecasting (Un)Reliable Realized Covariances for More Reliable Financial Decisions," CREATES Research Papers 2016-10, Department of Economics and Business Economics, Aarhus University.
    19. Eliana González & . Luis F. Melo & Viviana Monroy & Brayan Rojas, 2009. "A Dynamic Factor Model for the Colombian Inflation," Borradores de Economia 549, Banco de la Republica de Colombia.
    20. Jan J. J. Groen & Richard Paap & Francesco Ravazzolo, 2013. "Real-Time Inflation Forecasting in a Changing World," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 29-44, January.
    21. Rebecca Stuart, 2020. "Monetary regimes, the term structure and business cycles in Ireland, 1972–2018," Manchester School, University of Manchester, vol. 88(5), pages 731-748, September.
    22. Michiel De Pooter & Francesco Ravazzolo & Dick van Dijk, 2010. "Term structure forecasting using macro factors and forecast combination," International Finance Discussion Papers 993, Board of Governors of the Federal Reserve System (U.S.).
    23. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    24. Fady Barsoum, 2015. "Point and Density Forecasts Using an Unrestricted Mixed-Frequency VAR Model," Working Paper Series of the Department of Economics, University of Konstanz 2015-19, Department of Economics, University of Konstanz.
    25. Stefano Giglio & Ian Dew-Becker & David Berger, 2017. "Uncertainty Shocks as Second-Moment News Shocks," 2017 Meeting Papers 403, Society for Economic Dynamics.
    26. Magdalena Grothe & Aidan Meyler, 2018. "Inflation Forecasts: Are Market-Based and Survey-Based Measures Informative?," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 9(1), pages 171-188, January.
    27. Alfred A. Haug & Christie Smith, 2007. "Local linear impulse responses for a small open economy," Working Papers 0707, University of Otago, Department of Economics, revised Apr 2007.
    28. Joel Hasbrouck, 2021. "Price Discovery in High Resolution," Journal of Financial Econometrics, Oxford University Press, vol. 19(3), pages 395-430.
    29. Carriero, Andrea & Marcellino, Massimiliano, 2007. "A comparison of methods for the construction of composite coincident and leading indexes for the UK," International Journal of Forecasting, Elsevier, vol. 23(2), pages 219-236.
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  113. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2004. "Forecasting Macroeconomic Variables for the Acceding Countries," Working Papers 260, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Working Papers 334, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    2. Anindya Banerjee & Victor Bystrov & Paul Mizen, 2010. "Interest rate pass-through in the major European economies - the role of expectations," Discussion Papers 10-07, Department of Economics, University of Birmingham.
    3. Henry, Jerome & Marcellino, Massimiliano & Angelini, Elena, 2004. "Interpolation and Backdating with A Large Information Set," CEPR Discussion Papers 4533, C.E.P.R. Discussion Papers.
    4. O. De Bandt & E. Michaux & C. Bruneau & A. Flageollet, 2007. "Forecasting inflation using economic indicators: the case of France," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(1), pages 1-22.
    5. Christian Schulz, 2008. "Forecasting economic activity for Estonia : The application of dynamic principal component analyses," Bank of Estonia Working Papers 2008-02, Bank of Estonia, revised 30 Oct 2008.
    6. Victor Bystrov, 2006. "Forecasting Emerging Market Indicators: Brazil and Russia," Economics Working Papers ECO2006/12, European University Institute.
    7. Harm Bandholz, 2005. "New Composite Leading Indicators for Hungary and Poland," ifo Working Paper Series 3, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.

  114. Artis, Michael & Marcellino, Massimiliano & Proietti, Tommaso, 2004. "Characterizing the Business Cycle for Accession Countries," CEPR Discussion Papers 4457, C.E.P.R. Discussion Papers.

    Cited by:

    1. Hanus, Lubos & Vacha, Lukas, 2015. "Business cycle synchronization of the Visegrad Four and the European Union," FinMaP-Working Papers 42, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    2. Ivan Todorov, 2012. "European Economic Integration Theories and Criteria," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 2, pages 131-152.
    3. Konstantins Benkovskis, 2006. "The Effect of Latvian Pension Reform on Savings and Government Budget," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 6(1), pages 3-21, July.
    4. Lubos Hanus & Lukas Vacha, 2015. "Business cycle synchronization within the European Union: A wavelet cohesion approach," Papers 1506.03106, arXiv.org, revised Feb 2016.
    5. Nektarios Aslanidis, 2010. "Business Cycle Synchronization Between The Ceec And The Euro‐Area: Evidence From Threshold Seemingly Unrelated Regressions," Manchester School, University of Manchester, vol. 78(6), pages 538-555, December.
    6. Aslanidis, Nektarios, 2007. "Business Cycle Regimes in CEECs Production: A Threshold SURE Approach," Working Papers 2072/5318, Universitat Rovira i Virgili, Department of Economics.
    7. Rátfai, Attila & Benczúr, Péter, 2005. "Economic Fluctuations in Central and Eastern Europe: The Facts," CEPR Discussion Papers 4846, C.E.P.R. Discussion Papers.
    8. Eickmeier, Sandra & Breitung, Jörg, 2005. "How synchronized are central and east European economies with the euro area? Evidence from a structural factor model," Discussion Paper Series 1: Economic Studies 2005,20, Deutsche Bundesbank.
    9. Veaceslav Grigoras & Irina Eusignia Stanciu, 2016. "New evidence on the (de)synchronisation of business cycles: Reshaping the European business cycle," International Economics, CEPII research center, issue 147, pages 27-52.
    10. Ageliki Anagnostou & Ioannis Panteladis & Maria Tsiapa, 2015. "Disentangling different patterns of business cycle synchronicity in the EU regions," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 42(3), pages 615-641, August.
    11. Sanvi Avouyi-Dovi & Rafał Kierzenkowski & Catherine Lubochinsky, 2006. "Cycles réel et du crédit : convergence ou divergence ?. Une comparaison Pologne, Hongrie, République tchèque et zone euro," Revue économique, Presses de Sciences-Po, vol. 57(4), pages 851-879.
    12. Christos S. Savva & Kyriakos C. Neanidis & Denise R. Osborn, 2007. "Business Cycle Synchronization of the Euro Area with the New and Negotiating Member Countries," Centre for Growth and Business Cycle Research Discussion Paper Series 91, Economics, The University of Manchester.
    13. Olegs Tkacevs, 2006. "The Impact of Fiscal Policy on Prices: Does the Fiscal Theory of Price Level Matter in Latvia?," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 6(1), pages 23-36, July.
    14. Jesús Crespo-Cuaresma & Octavio Fernández-Amador, 2010. "Business cycle convergence in EMU: A first look at the second moment," Working Papers 2010-22, Faculty of Economics and Statistics, Universität Innsbruck.
    15. Peter Martey Addo & Monica Billio & Dominique Guegan, 2013. "Turning point chronology for the Euro-Zone: A Distance Plot Approach," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00803457, HAL.
    16. Konstantakopoulou, Ioanna & Tsionas, Efthymios G., 2014. "Half a century of empirical evidence of business cycles in OECD countries," Journal of Policy Modeling, Elsevier, vol. 36(2), pages 389-409.
    17. Bierbaumer-Polly, Jürgen & Huber, Peter & Huber, Petr, 2015. "The Impact of EU-Accession on Regional Business Cycle Synchronization and Sector Specialization," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113154, Verein für Socialpolitik / German Economic Association.
    18. Avouyi-Dovi, S. & Kierzenkowski, R. & Lubochinsky, C., 2006. "Are Business and Credit Cycles Converging or Diverging? A comparison of Poland, Hungary, the Czech Republic and the Euro Area," Working papers 144, Banque de France.
    19. Baher Ahmed Elgahry, 2020. "Regional and Interregional Business Cycle Comovement in Europe, Asia, and North America," Economics Bulletin, AccessEcon, vol. 40(4), pages 3088-3103.
    20. Jarko Fidrmuc & Iikka Korhonen, 2004. "A Meta-Analysis of Business Cycle Correlations between the Euro Area, CEECs and SEECs – What Do We Know?," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 2, pages 76-94.
    21. Jitka Poměnková & Svatopluk Kapounek & Roman Maršálek, 2011. "Comparison of methodological approaches to identify economic activity regularities in transition economy," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 59(7), pages 283-292.
    22. Zsolt Darvas & György Szapáry, 2006. "Business Cycle Synchronization in the Enlarged EU," Working Papers 0604, Department of Mathematical Economics and Economic Analysis, Corvinus University of Budapest.
    23. Mr. Ashoka Mody & Ms. Franziska L Ohnsorge, 2007. "Can Domestic Policies Influence Inflation?," IMF Working Papers 2007/257, International Monetary Fund.
    24. Stelios Bekiros & Duc Khuong Nguyen & Gazi Salah Uddin & Bo Sjö, 2014. "Business Cycle (De)Synchronization in the Aftermath of the Global Financial Crisis: Implications for the Euro Area," Working Papers 2014-437, Department of Research, Ipag Business School.
    25. Jamel Gatfaoui & Eric Girardin, 2015. "Comovement of Chinese provincial business cycles," Post-Print hal-01456105, HAL.
    26. Jürgen Bierbaumer-Polly & Werner Hölzl, 2016. "Business Cycle Dynamics and Firm Heterogeneity. Evidence for Austria Using Survey Data," WIFO Working Papers 504, WIFO.
    27. Gabriele Tondl & Iulia Traistaru-Siedschlag, 2006. "Regional growth cycle synchronisation with the Euro Area," Papers WP173, Economic and Social Research Institute (ESRI).
    28. António Afonso & Davide Furceri, 2007. "Business Cycle Synchronization and Insurance Mechanisms in the EU," Working Papers Department of Economics 2007/26, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    29. Iulia Siedschlag & Gabriele Tondl, 2011. "Regional output growth synchronisation with the Euro Area," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 38(2), pages 203-221, May.
    30. Maximo Camacho & Gabriel Perez-Quiros & Lorena Saiz & Universidad de Murcia, 2006. "Do european business cycles look like one $\_?$," Computing in Economics and Finance 2006 175, Society for Computational Economics.
    31. Jarko Fidrmuc & Iikka Korhonen, 2006. "Meta-Analysis of the Business Cycle Correlation between the Euro Area and the CEECs," CESifo Working Paper Series 1693, CESifo.
    32. Petr Rozmahel & Ladislava Issever Grochová & Marek Litzman, 2014. "The Effect of Asymmetries in Fiscal Policy Conducts on Business Cycle Correlation in the EU. WWWforEurope Working Paper No. 62," WIFO Studies, WIFO, number 47249, April.
    33. Maximo Camacho & Gabriel Perez-Quiros, 2004. "Are European business cycles close enough to be just one?," Computing in Economics and Finance 2004 16, Society for Computational Economics.
    34. Dinu. Marin & Marinas, Marius Corneliu & Socol Cristian & Socol, Aura Gabriela, 2012. "Clusterization, Persistence, Dependency and Volatility of Business Cycles in an Enlarged Euro Area," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 5-23, June.
    35. Dumitru, Ionut, 2009. "Adoptarea euro in Romania [Euro adoption in Romania]," MPRA Paper 18612, University Library of Munich, Germany.
    36. Faruk Balli & Syed Abul Basher & Hatice Ozer Balli, 2013. "International Income Risk-Sharing and the Global Financial Crisis of 2008- 2009," CAMA Working Papers 2013-02, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    37. Camacho, Maximo & Perez-Quiros, Gabriel & Saiz, Lorena, 2008. "Do European business cycles look like one?," Journal of Economic Dynamics and Control, Elsevier, vol. 32(7), pages 2165-2190, July.
    38. Fabrizio Carmignani, 2009. "Endogenous optimal currency areas: The case of the Central African Economic and Monetary Community," Discussion Papers Series 390, School of Economics, University of Queensland, Australia.
    39. Crespo-Cuaresma, Jesús & Fernández-Amador, Octavio, 2013. "Business cycle convergence in EMU: A second look at the second moment," Journal of International Money and Finance, Elsevier, vol. 37(C), pages 239-259.
    40. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2004. "Forecasting Macroeconomic Variables for the Acceding Countries," Working Papers 260, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    41. Luboš Hanus & Lukáš Vácha, 2020. "Growth cycle synchronization of the Visegrad Four and the European Union," Empirical Economics, Springer, vol. 58(4), pages 1779-1795, April.
    42. Jitka Poměnková, 2010. "An Alternative Approach to the Dating of Business Cycle: Nonparametric Kernel Estimation," Prague Economic Papers, Prague University of Economics and Business, vol. 2010(3), pages 251-272.
    43. Larry Sawers, 2006. "Inequality and the Transition: Regional Development in Lithuania," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 6(1), pages 37-51, July.
    44. Gammadigbé, Vigninou, 2012. "Les cycles économiques des pays de l'UEMOA: synchrones ou déconnectés? [Business cycles in the WAEMU countries: synchronous or disconnected?]," MPRA Paper 39400, University Library of Munich, Germany, revised Jun 2012.
    45. António Afonso & Davide Furceri, 2007. "Sectoral Business Cycle Synchronization in the European Union," Working Papers Department of Economics 2007/02, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    46. Juergen Bierbaumer-Polly, 2012. "Regional and Sectoral Business Cycles - Key Features for the Austrian economy," EcoMod2012 4074, EcoMod.
    47. Kierzenkowski, R. & Oung, V., 2007. "L’évolution des crédits à l’habitat en France : une grille d’analyse en termes de cycles," Working papers 172, Banque de France.
    48. Marcellino, Massimiliano & Banerjee, Anindya & Masten, Igor, 2005. "Forecasting macroeconomic variables for the new member states of the European Union," Working Paper Series 482, European Central Bank.
    49. Narayan K. Kishor & Kyriakos C. Neanidis, 2012. "What is Driving Financial Dollarization in Transition Economies? A Dynamic Factor Analysis," Centre for Growth and Business Cycle Research Discussion Paper Series 171, Economics, The University of Manchester.
    50. Pasquale Foresti & Ugo Marani & Giuseppe Piroli, 2013. "Macroeconomic Dynamics in Four Selected New Member States of the EU," EERI Research Paper Series EERI RP 2013/14, Economics and Econometrics Research Institute (EERI), Brussels.
    51. Petr Rozmahel, 2011. "Measuring the business cycles similarity and convergence trends in the Central and Eastern European countries towards the Eurozone with respect to some unclear methodological aspects," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 59(2), pages 237-250.
    52. Nikola Najman & Petr Rozmahel, 2013. "Business cycle coherence and OCA endogeneity testing during the integration period in the European Union," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 61(4), pages 1033-1040.
    53. Jurgita Jurgutyte, 2006. "Lithuania's Track to the Euro and the Endogeneity Hypothesis," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 6(1), pages 53-69, July.
    54. Fidrmuc, Jarko & Korhonen, Iikka, 2004. "A meta-analysis of business cycle correlation between the euro area and CEECs: What do we know - and who cares?," BOFIT Discussion Papers 20/2004, Bank of Finland Institute for Emerging Economies (BOFIT).
    55. Harm Bandholz, 2005. "New Composite Leading Indicators for Hungary and Poland," ifo Working Paper Series 3, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.

  115. Henry, Jerome & Marcellino, Massimiliano & Angelini, Elena, 2004. "Interpolation and Backdating with A Large Information Set," CEPR Discussion Papers 4533, C.E.P.R. Discussion Papers.

    Cited by:

    1. Massimiliano Marcellino, 2007. "Pooling‐Based Data Interpolation and Backdating," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(1), pages 53-71, January.
    2. Federica Ciocchetta & Wanda Cornacchia, 2019. "Assessing financial stability risks from the real estate market in Italy: an update," Questioni di Economia e Finanza (Occasional Papers) 493, Bank of Italy, Economic Research and International Relations Area.
    3. Jérôme Creel & Mehdi El Herradi, 2020. "Income inequality and monetary policy in the Euro Area," Sciences Po publications 20/2020, Sciences Po.
    4. Carriero, Andrea & Marcellino, Massimiliano, 2007. "A comparison of methods for the construction of composite coincident and leading indexes for the UK," International Journal of Forecasting, Elsevier, vol. 23(2), pages 219-236.
    5. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    6. Brunhes-Lesage, V. & Darné, O., 2008. "Why calculate a business sentiment indicator for services?," Quarterly selection of articles - Bulletin de la Banque de France, Banque de France, issue 13, pages 21-30, Autumn.
    7. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Working Papers 334, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    8. Lutz Kilian & Logan T. Lewis, 2011. "Does the Fed Respond to Oil Price Shocks?," Economic Journal, Royal Economic Society, vol. 121(555), pages 1047-1072, September.
    9. Darracq Pariès, Matthieu & Maurin, Laurent, 2008. "The role of country-specific trade and survey data in forecasting euro area manufacturing production: perspective from large panel factor models," Working Paper Series 894, European Central Bank.
    10. Ascari, Guido & Rankin, Neil, 2007. "Perpetual youth and endogenous labor supply: A problem and a possible solution," Journal of Macroeconomics, Elsevier, vol. 29(4), pages 708-723, December.
    11. Foroni, Claudia & Marcellino, Massimiliano & Schumacher, Christian, 2011. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," Discussion Paper Series 1: Economic Studies 2011,35, Deutsche Bundesbank.
    12. Tommaso Proietti & Alessandro Giovannelli, 2020. "Nowcasting Monthly GDP with Big Data: a Model Averaging Approach," CEIS Research Paper 482, Tor Vergata University, CEIS, revised 12 May 2020.
    13. Rafael R. S. Guimaraes, 2022. "Deep Learning Macroeconomics," Papers 2201.13380, arXiv.org.
    14. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72.
    15. Proietti, Tommaso, 2008. "Estimation of Common Factors under Cross-Sectional and Temporal Aggregation Constraints: Nowcasting Monthly GDP and its Main Components," MPRA Paper 6860, University Library of Munich, Germany.
    16. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    17. Camacho, Maximo & Lopez-Buenache, German, 2023. "Factor models for large and incomplete data sets with unknown group structure," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1205-1220.
    18. Klaus Wohlrabe, 2009. "Macroeconomic forecasting with mixed frequencies," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(21), pages 22-33, November.
    19. Angelini, Elena & Marcellino, Massimiliano, 2007. "Econometric analyses with backdated data: unified Germany and the euro area," Working Paper Series 752, European Central Bank.
    20. Henry, Jerome & Marcellino, Massimiliano & Angelini, Elena, 2004. "Interpolation and Backdating with A Large Information Set," CEPR Discussion Papers 4533, C.E.P.R. Discussion Papers.
    21. Luke Mosley & Idris A. Eckley & Alex Gibberd, 2022. "Sparse temporal disaggregation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2203-2233, October.
    22. Mateusz Pipień & Sylwia Roszkowska, 2015. "Szacunki kwartalnego PKB w polskich województwach," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 5, pages 145-169.
    23. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP," Economics Working Papers ECO2008/16, European University Institute.
    24. Jérôme Creel & Mehdi El Herradi, 2019. "Shocking aspects of monetary policy on income inequality in the euro area," Documents de Travail de l'OFCE 2019-15, Observatoire Francais des Conjonctures Economiques (OFCE).
    25. Mr. Troy D Matheson, 2011. "New Indicators for Tracking Growth in Real Time," IMF Working Papers 2011/043, International Monetary Fund.
    26. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2004. "Forecasting Macroeconomic Variables for the Acceding Countries," Working Papers 260, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    27. Hauber, Philipp & Schumacher, Christian, 2021. "Precision-based sampling with missing observations: A factor model application," Discussion Papers 11/2021, Deutsche Bundesbank.
    28. Schumacher Christian, 2011. "Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 28-49, February.
    29. Schumacher, Christian & Breitung, Jörg, 2008. "Real-time forecasting of German GDP based on a large factor model with monthly and quarterly data," International Journal of Forecasting, Elsevier, vol. 24(3), pages 386-398.
    30. Marcellino, Massimiliano & Banerjee, Anindya & Masten, Igor, 2005. "Forecasting macroeconomic variables for the new member states of the European Union," Working Paper Series 482, European Central Bank.
    31. Konstantin Kuck & Karsten Schweikert, 2021. "Forecasting Baden‐Württemberg's GDP growth: MIDAS regressions versus dynamic mixed‐frequency factor models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 861-882, August.
    32. Ralf Brüggemann & Jing Zeng, 2015. "Forecasting Euro-Area Macroeconomic Variables Using a Factor Model Approach for Backdating," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 22-39, February.
    33. Massimiliano Marcellino & Christian Schumacher, 2010. "Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 518-550, August.
    34. Hang Zhao & Jun Zhang & Xiaohui Wang & Hongxia Yuan & Tianlu Gao & Chenxi Hu & Jing Yan, 2021. "The Economy and Policy Incorporated Computing System for Social Energy and Power Consumption Analysis," Sustainability, MDPI, vol. 13(18), pages 1-18, September.
    35. Jennifer Castle & David Hendry & Oleg Kitov, 2013. "Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview," Economics Series Working Papers 674, University of Oxford, Department of Economics.
    36. Luke Mosley & Idris Eckley & Alex Gibberd, 2021. "Sparse Temporal Disaggregation," Papers 2108.05783, arXiv.org, revised Oct 2022.
    37. Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.

  116. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2003. "Leading Indicators for Euro Area Inflation and GDP Growth," CEPR Discussion Papers 3893, C.E.P.R. Discussion Papers.

    Cited by:

    1. Lamprou, Dimitra, 2016. "Nowcasting GDP in Greece: The impact of data revisions and forecast origin on model selection and performance," The Journal of Economic Asymmetries, Elsevier, vol. 14(PA), pages 93-102.
    2. Robert Lehmann, 2016. "Economic Growth and Business Cycle Forecasting at the Regional Level," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65.
    3. Albacete, Rebeca & Espasa, Antoni, 2005. "Forecasting inflation in the euro area using monthly time series models and quarterly econometric models," DES - Working Papers. Statistics and Econometrics. WS ws050401, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Chakravartti, Parma & Mundle, Sudipto, 2017. "An Automatic Leading Indicator Based Growth Forecast For 2016-17 and The Outlook Beyond," Working Papers 17/193, National Institute of Public Finance and Policy.
    5. Hahn, Elke & Zekaite, Zivile & de Bondt, Gabe, 2018. "ALICE: A new inflation monitoring tool," Working Paper Series 2175, European Central Bank.
    6. Christopher L. Gilbert & Duo Qin, 2007. "Representation in Econometrics: A Historical Perspective," Working Papers 583, Queen Mary University of London, School of Economics and Finance.
    7. Alain Kabundi & Rangan Gupta, 2009. "A Large Factor Model for Forecasting Macroeconomic Variables in South Africa," Working Papers 137, Economic Research Southern Africa.
    8. Mikosch, Heiner & Solanko, Laura, 2017. "Should one follow movements in the oil price or in money supply? Forecasting quarterly GDP growth in Russia with higher-frequency indicators," BOFIT Discussion Papers 19/2017, Bank of Finland Institute for Emerging Economies (BOFIT).
    9. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
    10. Kholodilin Konstantin Arkadievich & Siliverstovs Boriss, 2006. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 226(3), pages 234-259, June.
    11. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    12. Andrade, P. & Fourel, V. & Ghysels, E. & Idier, I., 2013. "The financial content of inflation risks in the euro area," Working papers 437, Banque de France.
    13. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Working Papers 334, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    14. Panopoulou, Ekaterini, 2009. "Financial variables and euro area growth: A non-parametric causality analysis," Economic Modelling, Elsevier, vol. 26(6), pages 1414-1419, November.
    15. Ciccarelli, Matteo & Mojon, Benoît, 2006. "Global Inflation," Kiel Working Papers 1337, Kiel Institute for the World Economy (IfW Kiel).
    16. Poza, Carlos & Monge, Manuel, 2020. "A real time leading economic indicator based on text mining for the Spanish economy. Fractional cointegration VAR and Continuous Wavelet Transform analysis," International Economics, Elsevier, vol. 163(C), pages 163-175.
    17. Eickmeier, Sandra & Breitung, Jörg, 2005. "How synchronized are central and east European economies with the euro area? Evidence from a structural factor model," Discussion Paper Series 1: Economic Studies 2005,20, Deutsche Bundesbank.
    18. Daniel Grenouilleau, 2004. "A sorted leading indicators dynamic (SLID) factor model for short-run euro-area GDP forecasting," European Economy - Economic Papers 2008 - 2015 219, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    19. Anindya Banerjee & Victor Bystrov & Paul Mizen, 2010. "Interest rate pass-through in the major European economies - the role of expectations," Discussion Papers 10-07, Department of Economics, University of Birmingham.
    20. Knut Are Aastveit & Karsten R. Gerdrup & Anne Sofie Jore & Leif Anders Thorsrud, 2011. "Nowcasting GDP in real-time: A density combination approach," Working Paper 2011/11, Norges Bank.
    21. Oliver Hülsewig & Johannes Mayr & Stéphane Sorbe, 2007. "Assessing the Forecast Properties of the CESifo World Economic Climate Indicator: Evidence for the Euro Area," ifo Working Paper Series 46, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    22. H. Burcu Gurcihan & Gonul Sengul & Arzu Yavuz, 2013. "A Quest for Leading Indicators of the Turkish Unemployment Rate," Working Papers 1341, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    23. Schumacher, Christian, 2009. "Factor forecasting using international targeted predictors: the case of German GDP," Discussion Paper Series 1: Economic Studies 2009,10, Deutsche Bundesbank.
    24. Jörg Breitung & Sandra Eickmeier, 2006. "Dynamic factor models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 27-42, March.
    25. Rangan Gupta & Alain Kabundi & Stephen M. Miller, 2009. "Forecasting the US Real House Price Index: Structural and Non-Structural Models with and without Fundamentals," Working papers 2009-42, University of Connecticut, Department of Economics.
    26. Katja Heinisch & Rolf Scheufele, 2018. "Bottom-up or direct? Forecasting German GDP in a data-rich environment," Empirical Economics, Springer, vol. 54(2), pages 705-745, March.
    27. Travaglini, Guido, 2011. "Climate change: where is the hockey stick? evidence from millennial-scale reconstructed and updated temperature time series," MPRA Paper 35565, University Library of Munich, Germany.
    28. Massimiliano Serati & Matteo Manera & Michele Plotegher, 2008. "Modelling electricity prices: from the state of the art to a draft of a new proposal," LIUC Papers in Economics 210, Cattaneo University (LIUC).
    29. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "Pooling versus model selection for nowcasting with many predictors: an application to German GDP," Discussion Paper Series 1: Economic Studies 2009,03, Deutsche Bundesbank.
    30. Schumacher, Christian & Marcellino, Massimiliano & Kuzin, Vladimir, 2009. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the Euro Area," CEPR Discussion Papers 7445, C.E.P.R. Discussion Papers.
    31. Poghosyan, K., 2012. "Structural and reduced-form modeling and forecasting with application to Armenia," Other publications TiSEM ad1a24c3-15e6-4f04-b338-3, Tilburg University, School of Economics and Management.
    32. Scharnagl, Michael & Schumacher, Christian, 2007. "Reconsidering the role of monetary indicators for euro area inflation from a Bayesian perspective using group inclusion probabilities," Discussion Paper Series 1: Economic Studies 2007,09, Deutsche Bundesbank.
    33. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    34. Kouwenberg, Roy & Zwinkels, Remco, 2014. "Forecasting the US housing market," International Journal of Forecasting, Elsevier, vol. 30(3), pages 415-425.
    35. Robert Lehmann & Klaus Wohlrabe, 2012. "Forecasting GDP at the Regional Level with Many Predictors," CESifo Working Paper Series 3956, CESifo.
    36. Angela Abbate & Sandra Eickmeier & Wolfgang Lemke & Massimiliano Marcellino, 2016. "The Changing International Transmission of Financial Shocks: Evidence from a Classical Time‐Varying FAVAR," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(4), pages 573-601, June.
    37. Poghosyan, Karen & Poghosyan, Ruben, 2021. "On the applicability of dynamic factor models for forecasting real GDP growth in Armenia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 61, pages 28-46.
    38. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
    39. Diron, Marie & Mojon, Benoît, 2005. "Forecasting the central bank's inflation objective is a good rule of thumb," Working Paper Series 564, European Central Bank.
    40. Vagenas, George & Vlachokyriakou, Eleni, 2012. "Olympic medals and demo-economic factors: Novel predictors, the ex-host effect, the exact role of team size, and the “population-GDP” model revisited," Sport Management Review, Elsevier, vol. 15(2), pages 211-217.
    41. Massimiliano Serati & Gianni Amisano, 2008. "Building composite leading indexes in a dynamic factor model framework: a new proposal," LIUC Papers in Economics 212, Cattaneo University (LIUC).
    42. Carlo Favero & Massimiliano Marcellino, 2005. "Modelling and Forecasting Fiscal Variables for the Euro Area," Working Papers 298, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    43. Oliver Hülsewig & Johannes Mayr & Timo Wollmershäuser, 2008. "Forecasting Euro Area Real GDP: Optimal Pooling of Information," CESifo Working Paper Series 2371, CESifo.
    44. Sarah Gelper & Christophe Croux, 2010. "On the Construction of the European Economic Sentiment Indicator," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(1), pages 47-62, February.
    45. Bhattacharya, Rudrani & Chakravarti, Parma & Mundle, Sudipto, 2018. "Forecasting India's Economic Growth: A Time-Varying Parameter Regression Approach," Working Papers 18/238, National Institute of Public Finance and Policy.
    46. Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting the French index of industrial production: A comparison from bridge and factor models," Economic Modelling, Elsevier, vol. 29(6), pages 2174-2182.
    47. Travaglini, Guido, 2014. "Testing the hockey-stick hypothesis by statistical analyses of a large dataset of proxy records," MPRA Paper 55835, University Library of Munich, Germany.
    48. Roffia, Barbara & Zaghini, Andrea, 2007. "Excess money growth and inflation dynamics," Working Paper Series 749, European Central Bank.
    49. Jun Wen & Samia Khalid & Hamid Mahmood & Xiuyun Yang, 2022. "Economic policy uncertainty and growth nexus in Pakistan: a new evidence using NARDL model," Economic Change and Restructuring, Springer, vol. 55(3), pages 1701-1715, August.
    50. Hwee Kwan Chow & Yijie Fei & Daniel Han, 2023. "Forecasting GDP with many predictors in a small open economy: forecast or information pooling?," Empirical Economics, Springer, vol. 65(2), pages 805-829, August.
    51. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP," Economics Working Papers ECO2008/16, European University Institute.
    52. Barhoumi, K. & Darné, O. & Ferrara, L., 2013. "Dynamic Factor Models: A review of the Literature ," Working papers 430, Banque de France.
    53. Duo Qin & Marie Anne Cagas & Geoffrey Ducanes & Nedelyn Magtibay-Ramos & Pilipinas Quising, 2007. "Automatic Leading Indicators (ALIs) versus Macro Econometric Structural Models (MESMs): Comparison of Inflation and GDP growth Forecasting," EcoMod2007 23900072, EcoMod.
    54. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "MIDAS versus mixed-frequency VAR: nowcasting GDP in the euro area," Discussion Paper Series 1: Economic Studies 2009,07, Deutsche Bundesbank.
    55. Rossi, Barbara & Sekhposyan, Tatevik, 2014. "Evaluating predictive densities of US output growth and inflation in a large macroeconomic data set," International Journal of Forecasting, Elsevier, vol. 30(3), pages 662-682.
    56. Antipa, Pamfili & Barhoumi, Karim & Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Journal of Policy Modeling, Elsevier, vol. 34(6), pages 864-878.
    57. Banbura, Marta & Rünstler, Gerhard, 2011. "A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP," International Journal of Forecasting, Elsevier, vol. 27(2), pages 333-346, April.
    58. Heather Anderson & Mardi Dungey & Denise R. Osborn & Farshid Vahid, 2007. "Constructing Historical Euro Area Data," CAMA Working Papers 2007-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    59. El-Shagi, Makram, 2011. "Inflation expectations: Does the market beat econometric forecasts?," The North American Journal of Economics and Finance, Elsevier, vol. 22(3), pages 298-319.
    60. Dovern, Jonas, 2006. "Predicting GDP components: do leading indicators increase predictability?," Kiel Advanced Studies Working Papers 436, Kiel Institute for the World Economy (IfW Kiel).
    61. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2005. "Leading Indicators for Euro‐area Inflation and GDP Growth," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 785-813, December.
    62. Anindya Banerjee & Bill Russell, 2006. "A markup model for forecasting inflation for the euro area," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(7), pages 495-511.
    63. Capistran, Carlos, 2006. "On comparing multi-horizon forecasts," Economics Letters, Elsevier, vol. 93(2), pages 176-181, November.
    64. Raffaele Mattera & Michelangelo Misuraca & Maria Spano & Germana Scepi, 2023. "Mixed frequency composite indicators for measuring public sentiment in the EU," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2357-2382, June.
    65. Schumacher Christian, 2011. "Forecasting with Factor Models Estimated on Large Datasets: A Review of the Recent Literature and Evidence for German GDP," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(1), pages 28-49, February.
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    68. Christian Schumacher, 2007. "Forecasting German GDP using alternative factor models based on large datasets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 271-302.
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    74. Qin, Duo & Cagas, Marie Anne & Ducanes, Geoffrey & Magtibay-Ramos, Nedelyn & Quising, Pilipinas, 2008. "Automatic leading indicators versus macroeconometric structural models: A comparison of inflation and GDP growth forecasting," International Journal of Forecasting, Elsevier, vol. 24(3), pages 399-413.
    75. Fichtner, Ferdinand & Rüffer, Rasmus & Schnatz, Bernd, 2009. "Leading indicators in a globalised world," Working Paper Series 1125, European Central Bank.
    76. Daniel Grenouilleau, 2006. "The Stacked Leading Indicators Dynamic Factor Model: A Sensitivity Analysis of Forecast Accuracy using Bootstrapping," European Economy - Economic Papers 2008 - 2015 249, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    77. Duo Qin & Marie Anne Cagas & Geoffrey Ducanes & Nedelyn Magtibay-Ramos & Pilipinas Quising, 2006. "Forecasting Inflation and GDP growth: Comparison of Automatic Leading Indicator (ALI) Method with Macro Econometric Structural Models (MESMs)," Working Papers 554, Queen Mary University of London, School of Economics and Finance.
    78. Giuseppe Munda, 2012. "Beyond GDP: Methodological and measurement issues in redefining “wealth”," UHE Working papers 2012_09, Universitat Autònoma de Barcelona, Departament d'Economia i Història Econòmica, Unitat d'Història Econòmica.
    79. Barhoumi, K. & Brunhes-Lesage, V. & Darné, O. & Ferrara, L. & Pluyaud, B. & Rouvreau, B., 2008. "Monthly forecasting of French GDP: A revised version of the OPTIM model," Working papers 222, Banque de France.
    80. Giuseppe Munda, 2015. "Beyond Gdp: An Overview Of Measurement Issues In Redefining ‘Wealth’," Journal of Economic Surveys, Wiley Blackwell, vol. 29(3), pages 403-422, July.
    81. El-Shagi, Makram, 2009. "Inflation Expectations: Does the Market Beat Professional Forecasts?," IWH Discussion Papers 16/2009, Halle Institute for Economic Research (IWH).
    82. Panopoulou, Ekaterini, 2007. "Predictive financial models of the euro area: A new evaluation test," International Journal of Forecasting, Elsevier, vol. 23(4), pages 695-705.
    83. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    84. Mehdi Seraj & Pejman Bahramian & Abdulkareem Alhassan & Rasool Dehghanzadeh Shahabad, 2020. "The validity of Rodrik’s conclusion on real exchange rate and economic growth: factor priority evidence from feature selection approach," Palgrave Communications, Palgrave Macmillan, vol. 6(1), pages 1-6, December.

  117. Artis, Michael & Marcellino, Massimiliano & Galvão, Ana Beatriz, 2003. "The Transmission Mechanism in a Changing World," CEPR Discussion Papers 4014, C.E.P.R. Discussion Papers.

    Cited by:

    1. Aslanidis, Nektarios & Christiansen, Charlotte, 2012. "Smooth transition patterns in the realized stock–bond correlation," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 454-464.
    2. Michaelides, Panayotis G. & Papageorgiou, Theofanis & Vouldis, Angelos T., 2013. "Business cycles and economic crisis in Greece (1960–2011): A long run equilibrium analysis in the Eurozone," Economic Modelling, Elsevier, vol. 31(C), pages 804-816.
    3. Eickmeier, Sandra, 2006. "Comovements and heterogeneity in the Comovements and heterogeneity in the dynamic factor model," Discussion Paper Series 1: Economic Studies 2006,31, Deutsche Bundesbank.
    4. Goggin, Jean & Siedschlag, Iulia, 2009. "International Transmission of Business Cycles Between Ireland and its Trading Partners," Papers WP279, Economic and Social Research Institute (ESRI).
    5. Jiang, Chun & Li, Xiao-Lin & Chang, Hsu-Ling & Su, Chi-Wei, 2013. "Uncovered interest parity and risk premium convergence in Central and Eastern European countries," Economic Modelling, Elsevier, vol. 33(C), pages 204-208.
    6. Marco Duenas & Giorgio Fagiolo, 2011. "Modeling the International-Trade Network: A Gravity Approach," LEM Papers Series 2011/25, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    7. Eickmeier, Sandra & Metiu, Norbert & Prieto, Esteban, 2016. "Time-varying Volatility, Financial Intermediation and Monetary Policy," IWH Discussion Papers 19/2016, Halle Institute for Economic Research (IWH).
    8. Ageliki Anagnostou & Ioannis Panteladis & Maria Tsiapa, 2015. "Disentangling different patterns of business cycle synchronicity in the EU regions," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 42(3), pages 615-641, August.
    9. Jan Babecky & Oxana Babetskaia-Kukharchuk & Kamil Galuscak & Dana Hajkova & Jaroslav Hermanek & Tomas Holub & Roman Horvath & Petr Jakubik & Lubos Komarek & Zlatuse Komarkova & Petr Kral & Filip Novot, 2008. "Analyses of the Czech Republic's Current Economic Alignment with the Euro Area 2008," Occasional Publications - Edited Volumes, Czech National Bank, number as08 edited by Dana Hajkova, January.
    10. Michaelides, Panayotis G. & Tsionas, Efthymios G. & Konstantakis, Konstantinos N., 2018. "Debt Crisis in Europe (2001-2015): A Network General Equilibrium GVAR approach," MPRA Paper 89998, University Library of Munich, Germany.
    11. Eickmeier, Sandra, 2004. "Business Cycle Transmission from the US to Germany: a Structural Factor Approach," Discussion Paper Series 1: Economic Studies 2004,12, Deutsche Bundesbank.
    12. Pär Stockhammar & Pär Österholm, 2016. "Effects of US policy uncertainty on Swedish GDP growth," Empirical Economics, Springer, vol. 50(2), pages 443-462, March.
    13. Gefang Deborah & Strachan Rodney, 2009. "Nonlinear Impacts of International Business Cycles on the U.K. -- A Bayesian Smooth Transition VAR Approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(1), pages 1-33, December.
    14. P�r Österholm & P�r Stockhammar, 2014. "The euro crisis and Swedish GDP growth - a study of spillovers," Applied Economics Letters, Taylor & Francis Journals, vol. 21(16), pages 1105-1110, November.
    15. Eickmeier, Sandra & Moll, Katharina, 2008. "The global dimension of inflation: evidence from factor-augmented Phillips curves," Discussion Paper Series 1: Economic Studies 2008,16, Deutsche Bundesbank.
    16. Jiang, Chun & Jian, Na & Liu, Tie-Ying & Su, Chi-Wei, 2016. "Purchasing power parity and real exchange rate in Central Eastern European countries," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 349-358.
    17. Eickmeier, Sandra, 2009. "Analyse der Übertragung US-amerikanischer Schocks auf Deutschland auf Basis eines FAVAR," Working Papers 04/2009, German Council of Economic Experts / Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung.
    18. Erden, Lutfi & Ozkan, Ibrahim, 2014. "Determinants of international transmission of business cycles to Turkish economy," Economic Modelling, Elsevier, vol. 36(C), pages 383-390.
    19. Iulia Traistaru-Siedschlag, 2006. "Macroeconomic Differentials and Adjustment in the Euro Area," Papers WP175, Economic and Social Research Institute (ESRI).
    20. Angela Abbate & Sandra Eickmeier & Wolfgang Lemke & Massimiliano Marcellino, 2016. "The Changing International Transmission of Financial Shocks: Evidence from a Classical Time‐Varying FAVAR," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(4), pages 573-601, June.
    21. Eric Girardin, 2004. "Regime-Dependent Synchronization of Growth Cycles between Japan and East Asia," Asian Economic Papers, MIT Press, vol. 3(3), pages 147-176.
    22. Giorgio Fagiolo, 2009. "The International-Trade Network: Gravity Equations and Topological Properties," Papers 0908.2086, arXiv.org.
    23. Galvão, Ana Beatriz C., 2003. "Multivariate Threshold Models: TVARs and TVECMs," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 23(1), May.
    24. Buckle, Robert A. & Kim, Kunhong & Kirkham, Heather & McLellan, Nathan & Sharma, Jarad, 2007. "A structural VAR business cycle model for a volatile small open economy," Economic Modelling, Elsevier, vol. 24(6), pages 990-1017, November.
    25. Gabriele Tondl & Iulia Traistaru-Siedschlag, 2006. "Regional growth cycle synchronisation with the Euro Area," Papers WP173, Economic and Social Research Institute (ESRI).
    26. Marcus Miller & Olli Castrén & Lei Zhang, 2007. "'Irrational exuberance' and capital flows for the US New Economy: a simple global model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 12(1), pages 89-105.
    27. Iulia Siedschlag & Gabriele Tondl, 2011. "Regional output growth synchronisation with the Euro Area," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 38(2), pages 203-221, May.
    28. Tomas Adam & Oxana Babecka Kucharcukova & Jan Babecky & Kamil Galuscak & Tomas Holub & Eva Hromadkova & Narcisa Liliana Kadlcakova & Lubos Komarek & Zlatuse Komarkova & Petr Kral & Ivana Kubicova & Ji, 2012. "Analyses of the Czech Republic's Current Economic Alignment with the Euro Area 2012," Occasional Publications - Edited Volumes, Czech National Bank, number as12 edited by Romana Zamazalova & Jakub Mateju, January.
    29. Calza Alessandro & Sousa João, 2006. "Output and Inflation Responses to Credit Shocks: Are There Threshold Effects in the Euro Area?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(2), pages 1-21, May.
    30. Hilberg, Björn & Grill, Michael & Metiu, Norbert, 2016. "Credit constraints and the international propagation of US financial shocks," Working Paper Series 1954, European Central Bank.
    31. Su, Chi-Wei & Chang, Hsu-Ling & Chang, Tsangyao & Yin, Kedong, 2014. "Monetary convergence in East Asian countries relative to China," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 228-237.
    32. Miller, Marcus & Castrén, Olli & Zhang, Lei, 2005. "Capital flows and the US "New Economy": consumption smoothing and risk exposure," Working Paper Series 459, European Central Bank.
    33. Gerhard Fenz & Martin Schneider, 2008. "Transmission of business cycle shocks between the US and the euro area," Working Papers 145, Oesterreichische Nationalbank (Austrian Central Bank).
    34. Michaelides, Panayotis G. & Tsionas, Efthymios G. & Konstantakis, Konstantinos N., 2018. "Debt dynamics in Europe: A Network General Equilibrium GVAR approach," Journal of Economic Dynamics and Control, Elsevier, vol. 93(C), pages 175-202.
    35. Mastromarco Camilla & Laura Serlenga & Yongcheol Shin, 2013. "Globalisation and technological convergence in the EU," Journal of Productivity Analysis, Springer, vol. 40(1), pages 15-29, August.
    36. Stockhammar, Pär & Österholm, Pär, 2016. "The Impact of US Uncertainty Shocks on Small Open Economies," Working Papers 2016:5, Örebro University, School of Business.
    37. Miller, Marcus, 2005. "World Finance and the US 'New Economy': Risk Sharing and Risk Exposure," CEPR Discussion Papers 4855, C.E.P.R. Discussion Papers.
    38. Michaelides, Panayotis G. & Papageorgiou, Theofanis, 2012. "On the transmission of economic fluctuations from the USA to EU-15 (1960–2011)," Journal of Economics and Business, Elsevier, vol. 64(6), pages 427-438.
    39. Metiu, Norbert & Hilberg, Björn & Grill, Michael, 2015. "Financial frictions and global spillovers," Discussion Papers 04/2015, Deutsche Bundesbank.
    40. Eickmeier, Sandra, 2005. "Common stationary and non-stationary factors in the euro area analyzed in a large-scale factor model," Discussion Paper Series 1: Economic Studies 2005,02, Deutsche Bundesbank.
    41. Barrett, Alan & Bergin, Adele & FitzGerald, John & Traistaru-Siedschlag, Iulia, 2006. "Economic Assessment of the Euro Area: Forecasts and Policy Analysis, Autumn Report 2006," Research Series, Economic and Social Research Institute (ESRI), number sustat22, June.

  118. Oscar Jorda & Massimiliano Marcellino, 2003. "Time-Scale Transformations of Discrete-Time Processes," Working Papers 65, University of California, Davis, Department of Economics.

    Cited by:

    1. Andrea, SILVESTRINI, 2005. "Temporal aggregaton of univariate linear time series models," Discussion Papers (ECON - Département des Sciences Economiques) 2005044, Université catholique de Louvain, Département des Sciences Economiques.
    2. Andrea Silvestrini & David Veredas, 2008. "Temporal aggregation of univariate and multivariate time series models: A survey," Temi di discussione (Economic working papers) 685, Bank of Italy, Economic Research and International Relations Area.
    3. Shigeru Fujita & Garey Ramey, 2006. "The cyclicality of job loss and hiring," Working Papers 06-17, Federal Reserve Bank of Philadelphia.
    4. Kunst, Robert M. & Franses, Philip Hans, 2010. "Asymmetric Time Aggregation and its Potential Benefits for Forecasting Annual Data," Economics Series 252, Institute for Advanced Studies.

  119. George Kapetanios & Massimiliano Marcellino, 2003. "A Comparison of Estimation Methods for Dynamic Factor Models of Large Dimensions," Working Papers 489, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Farmer, Roger & Henry, Jerome & Marcellino, Massimiliano & Beyer, Andreas, 2005. "Factor Analysis in a New-Keynesian Model," CEPR Discussion Papers 5266, C.E.P.R. Discussion Papers.
    2. Todd E. Clark, 2003. "Disaggregate evidence on the persistence of consumer price inflation," Research Working Paper RWP 03-11, Federal Reserve Bank of Kansas City.
    3. Darracq Pariès, Matthieu & Maurin, Laurent, 2008. "The role of country-specific trade and survey data in forecasting euro area manufacturing production: perspective from large panel factor models," Working Paper Series 894, European Central Bank.
    4. Eickmeier, Sandra & Breitung, Jörg, 2005. "How synchronized are central and east European economies with the euro area? Evidence from a structural factor model," Discussion Paper Series 1: Economic Studies 2005,20, Deutsche Bundesbank.
    5. Andrea Cipollini & George Kapetanios, 2005. "Forecasting Financial Crises and Contagion in Asia Using Dynamic Factor Analysis," Working Papers 538, Queen Mary University of London, School of Economics and Finance.
    6. Andrea Cipollini & Nektarios Aslanidis, 2007. "Leading indicator properties of US high-yield credit spreads," Center for Economic Research (RECent) 006, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    7. George Kapetanios, 2004. "Dynamic Factor Extraction of Cross-Sectional Dependence in Panel Unit Root Tests," Working Papers 509, Queen Mary University of London, School of Economics and Finance.
    8. Eickmeier, Sandra, 2004. "Business Cycle Transmission from the US to Germany: a Structural Factor Approach," Discussion Paper Series 1: Economic Studies 2004,12, Deutsche Bundesbank.
    9. Eickmeier, Sandra & Moll, Katharina, 2008. "The global dimension of inflation: evidence from factor-augmented Phillips curves," Discussion Paper Series 1: Economic Studies 2008,16, Deutsche Bundesbank.
    10. Jan J.J. Groen & George Kapetanios, 2008. "Revisiting Useful Approaches to Data-Rich Macroeconomic Forecasting," Working Papers 624, Queen Mary University of London, School of Economics and Finance.
    11. Nektarios Aslanidis & Andrea Cipollini, 2007. "Leading indicator properties of the US corporate spreads," Money Macro and Finance (MMF) Research Group Conference 2006 115, Money Macro and Finance Research Group.
    12. Andrea Cipollini & George Kapetanios, 2004. "A Stochastic Variance Factor Model for Large Datasets and an Application to S&P Data," Working Papers 506, Queen Mary University of London, School of Economics and Finance.
    13. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
    14. Andrea Cipollini & George Kapetanios, 2003. "A Dynamic Factor Analysis of Financial Contagion in Asia," Working Papers 498, Queen Mary University of London, School of Economics and Finance.
    15. Dées, Stéphane & Burgert, Matthias, 2008. "Forecasting world trade: direct versus "bottom-up" approaches," Working Paper Series 882, European Central Bank.
    16. Moneta, Fabio & Rüffer, Rasmus, 2006. "Business cycle synchronisation in East Asia," Working Paper Series 671, European Central Bank.
    17. Lucia Alessi & Matteo Barigozzi & Marco Capasso, 2006. "A Dynamic Factor Analysis of Business Cycle on Firm-Level Data," LEM Papers Series 2006/27, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    18. Jana Eklund & George Kapetanios, 2008. "A Review of Forecasting Techniques for Large Data Sets," Working Papers 625, Queen Mary University of London, School of Economics and Finance.
    19. Eickmeier, Sandra, 2005. "Common stationary and non-stationary factors in the euro area analyzed in a large-scale factor model," Discussion Paper Series 1: Economic Studies 2005,02, Deutsche Bundesbank.

  120. Artis, Michael & Marcellino, Massimiliano & Proietti, Tommaso, 2003. "Dating the Euro Area Business Cycle," CEPR Discussion Papers 3696, C.E.P.R. Discussion Papers.

    Cited by:

    1. Douglas Sutherland & Peter Hoeller & Balázs Égert & Oliver Röhn, 2010. "Counter-cyclical Economic Policy," OECD Economics Department Working Papers 760, OECD Publishing.
    2. Don Harding & Adrian Pagan, 2006. "Measurement of Business Cycles," Department of Economics - Working Papers Series 966, The University of Melbourne.
    3. Mattia Guerini & Duc Thi Luu & Mauro Napoletano, 2019. "Synchronization Patterns in the European Union," GREDEG Working Papers 2019-30, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    4. Skikiewicz Robert & Garczarczyk Józef, 2018. "Cyclical Fluctuations in the Banking Services Market and the Changes in the Situation of Entities from the Financial Services Sector," Central European Economic Journal, Sciendo, vol. 5(52), pages 118-129, January.
    5. Emanuel Mönch & Harald Uhlig, 2005. "Towards a Monthly Business Cycle Chronology for the Euro Area," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(1), pages 43-69.
    6. Valentina Aprigliano & Lorenzo Bencivelli, 2013. "Ita-coin: a new coincident indicator for the Italian economy," Temi di discussione (Economic working papers) 935, Bank of Italy, Economic Research and International Relations Area.
    7. Carriero, Andrea & Marcellino, Massimiliano, 2007. "A comparison of methods for the construction of composite coincident and leading indexes for the UK," International Journal of Forecasting, Elsevier, vol. 23(2), pages 219-236.
    8. Andreas Billmeier, 2009. "Ghostbusting: which output gap really matters?," International Economics and Economic Policy, Springer, vol. 6(4), pages 391-419, December.
    9. Klaus Abberger, 2006. "Qualitative Business Surveys in Manufacturing and Industrial Production - What can be Learned from Industry Branch Results?," ifo Working Paper Series 31, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    10. João Loureiro & Manuel M. F. Martins & Ana Paula Ribeiro, 2009. "Cape Verde: The Case for Euroization," FEP Working Papers 317, Universidade do Porto, Faculdade de Economia do Porto.
    11. Monica Billio & Massimiliano Caporin & Guido Cazzavillan, 2008. "Dating EU15 monthly business cycle jointly using GDP and IPI," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2007(3), pages 333-366.
    12. Jean-Paul Pollin & Jean-Luc Gaffard, 2013. "Pourquoi faut-il séparer les activités bancaires ?," Post-Print hal-00972749, HAL.
    13. Tommaso Proietti, 2009. "On the Model-Based Interpretation of Filters and the Reliability of Trend-Cycle Estimates," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 186-208.
    14. Mario Cunha, 2010. "Modelling the Cyclical Behaviour of Wine Production in the Douro Region Using a Time-Varying Parameters Approach," Working Papers 2010.1, International Network for Economic Research - INFER.
    15. Jang, Tae-Seok & Sacht, Stephen, 2014. "Animal spirits and the business cycle: Empirical evidence from moment matching," Economics Working Papers 2014-06, Christian-Albrechts-University of Kiel, Department of Economics.
    16. Massmann, Michael & Mitchell, James, 2003. "Reconsidering the evidence: Are Eurozone business cycles converging," ZEI Working Papers B 05-2003, University of Bonn, ZEI - Center for European Integration Studies.
    17. Avouyi-Dovi, S. & Matheron, J., 2003. "Interactions between business cycles, stock market cycles and interest rates: the stylised facts," Financial Stability Review, Banque de France, issue 3, pages 80-99, November.
    18. Balázs Égert & Douglas Sutherland, 2014. "The Nature of Financial and Real Business Cycles: The Great Moderation and Banking Sector Pro-Cyclicality," Scottish Journal of Political Economy, Scottish Economic Society, vol. 61(1), pages 98-117, February.
    19. Klaus Abberger & Wolfgang Nierhaus, 2010. "Drei Monitorsysteme zur Analyse der sächsischen Industriekonjunktur," ifo Dresden berichtet, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 17(06), pages 33-39, December.
    20. Reichlin, Lucrezia & Giannone, Domenico & Lenza, Michele, 2009. "Business Cycles in the Euro Area," CEPR Discussion Papers 7124, C.E.P.R. Discussion Papers.
    21. Bengoechea, Pilar & Camacho, Maximo & Perez-Quiros, Gabriel, 2006. "A useful tool for forecasting the Euro-area business cycle phases," International Journal of Forecasting, Elsevier, vol. 22(4), pages 735-749.
    22. V. Colombo, 2020. "Opening the Red Budget Box: Nonlinear Effects of a Tax Shock in the UK," Working Papers wp1142, Dipartimento Scienze Economiche, Universita' di Bologna.
    23. Andrea Carriero & Massimiliano Marcellino, 2007. "Monitoring the Economy of the Euro Area: A Comparison of Composite Coincident Indexes," Working Papers 319, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    24. Peter Martey Addo & Monica Billio & Dominique Guegan, 2013. "Turning point chronology for the Euro-Zone: A Distance Plot Approach," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00803457, HAL.
    25. Jakob De Haan & Robert Inklaar & Richard Jong‐A‐Pin, 2008. "Will Business Cycles In The Euro Area Converge? A Critical Survey Of Empirical Research," Journal of Economic Surveys, Wiley Blackwell, vol. 22(2), pages 234-273, April.
    26. Monica Billio & Jacques Anas & Laurent Ferrara & Marco Lo Duca, 2007. "A turning point chronology for the Euro-zone," Working Papers 2007_33, Department of Economics, University of Venice "Ca' Foscari".
    27. Canova, Fabio & Ciccarelli, Matteo & Ortega, Eva, 2007. "Similarities and convergence in G-7 cycles," Journal of Monetary Economics, Elsevier, vol. 54(3), pages 850-878, April.
    28. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72.
    29. Jesus Crespo Cuaresma & Maria Antoinette Dimitz & Doris Ritzberger-Grünwald, 2002. "Growth, Convergence and EU Membership," Working Papers 62, Oesterreichische Nationalbank (Austrian Central Bank).
    30. Stijn Claessens & M. Ayhan Kose & Marco E. Terrones, 2009. "What happens during recessions, crunches and busts? [Business cycles for G-7 and European countries]," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 24(60), pages 653-700.
    31. Maria Antoinette Silgoner & Jesús Crespo-Cuaresma & Gerhard Reitschuler, 2003. "The Fiscal Smile: The Effectiveness and Limits of Fiscal Stabilizers," IMF Working Papers 2003/182, International Monetary Fund.
    32. Michael Artis & Massimiliano Marcellino & Tommaso Proietti, 2004. "Characterising the Business Cycle for Accession Countries," Econometrics 0403006, University Library of Munich, Germany.
    33. Andrea Carriero & Massimiliano Marcellino, 2011. "Sectoral Survey‐based Confidence Indicators for Europe," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(2), pages 175-206, April.
    34. Erden, Lutfi & Ozkan, Ibrahim, 2014. "Determinants of international transmission of business cycles to Turkish economy," Economic Modelling, Elsevier, vol. 36(C), pages 383-390.
    35. Grech, Aaron George, 2013. "Adapting the Hodrick-Prescott Filter for Very Small Open Economies," MPRA Paper 48803, University Library of Munich, Germany.
    36. Pedro André Cerqueira, 2010. "A Closer Look at the World Business Cycle Synchronization," GEMF Working Papers 2010-21, GEMF, Faculty of Economics, University of Coimbra.
    37. Carlo Favero & Massimiliano Marcellino, 2005. "Modelling and Forecasting Fiscal Variables for the Euro Area," Working Papers 298, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    38. Klaus Abberger, 2005. "A comparison of time series for producer prices and the price expectations in manufacturing in the Ifo Business Survey," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 58(14), pages 50-51, July.
    39. Bovi, M., 2005. "Economic Clubs and European Commitment. Evidence from the International Business Cycles," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 2(2), pages 101-122.
    40. Alex Cukierman, 2009. "The Limits of Transparency," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 38(1‐2), pages 1-37, February.
    41. Artis, Michael, 2002. "Dating the Business Cycle in Britain," National Institute Economic Review, National Institute of Economic and Social Research, vol. 182, pages 90-95, October.
    42. Grech, Aaron George, 2014. "Investigating potential output using the Hodrick-Prescott filter: an application for Malta," MPRA Paper 57131, University Library of Munich, Germany.
    43. Willie Lahari, 2011. "Assessing Business Cycle Synchronisation - Prospects for a Pacific Islands Currency Union," Working Papers 1110, University of Otago, Department of Economics, revised Oct 2011.
    44. Legrand, Romain, 2014. "Euro introduction: Has there been a structural change? Study on 10 European Union countries," Economic Modelling, Elsevier, vol. 40(C), pages 136-151.
    45. Vincent, BODART & Konstantin A., KHOLODILIN & Fati, SHADMAN-MEHTA, 2003. "Dating and Forecasting the Belgian Business Cycle," LIDAM Discussion Papers IRES 2003018, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    46. Vítor Castro & Pedro A. Cerqueira & Rodrigo Martins, 2024. "Is There a Pervasive World Real Credit Cycle?," Open Economies Review, Springer, vol. 35(1), pages 99-119, February.
    47. Hasan Engin Duran, 2014. "Short-Run Dynamics of Income Disparities and Regional Cycle Synchronization in the U.S," Growth and Change, Wiley Blackwell, vol. 45(2), pages 292-332, June.
    48. Parnaudeau, Miia, 2008. "European Business Fluctuations in the Austrian Framework," MPRA Paper 25046, University Library of Munich, Germany.
    49. Pilar Bengoechea & Gabriel Pérez Quirós, 2004. "A useful tool to identify recessions in the euro area," European Economy - Economic Papers 2008 - 2015 215, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission.
    50. Sylvia Kaufmann, 2008. "Dating and forecasting turning points by Bayesian clustering with dynamic structure: A suggestion with an application to Austrian data," Working Papers 144, Oesterreichische Nationalbank (Austrian Central Bank).
    51. Titelman Kardonsky, Daniel & Pérez Caldentey, Esteban & Carvallo, Pablo, 2013. "Weak expansions: a distinctive feature of the business cycle in Latin America and the Caribbean," Financiamiento para el Desarrollo 5224, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    52. Hugues Pirotte & Celine Vaessen, 2008. "Residual value risk in the leasing industry: A European case," The European Journal of Finance, Taylor & Francis Journals, vol. 14(2), pages 157-177.
    53. Michael J. Artis & Jarko Fidrmuc & Johann Scharler, 2008. "The transmission of business cycles Implications for EMU enlargement1," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 16(3), pages 559-582, July.
    54. Heather Anderson & Mardi Dungey & Denise R. Osborn & Farshid Vahid, 2007. "Constructing Historical Euro Area Data," CAMA Working Papers 2007-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    55. Franses, Philip Hans & Kunst, Robert M., 2007. "Analyzing a panel of seasonal time series: Does seasonality in industrial production converge across Europe?," Economic Modelling, Elsevier, vol. 24(6), pages 954-968, November.
    56. Fabrizio Carmignani, 2009. "Endogenous optimal currency areas: The case of the Central African Economic and Monetary Community," Discussion Papers Series 390, School of Economics, University of Queensland, Australia.
    57. Jürgen Bierbaumer-Polly, 2012. "Regionale Konjunkturzyklen in Österreich," WIFO Monatsberichte (monthly reports), WIFO, vol. 85(11), pages 833-848, November.
    58. Klaus Abberger & Wolfgang Nierhaus, 2011. "Ifo Business Climate, output and earnings in industry and trade," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 64(03), pages 21-24, February.
    59. Sumru Altuğ & Melike Bildirici, 2010. "Business Cycles around the Globe: A Regime Switching Approach," Working Papers 0032, Yildiz Technical University, Department of Economics, revised Mar 2010.
    60. Mikael Bask & Jarko Fidrmuc, 2009. "Fundamentals and Technical Trading: Behavior of Exchange Rates in the CEECs," Open Economies Review, Springer, vol. 20(5), pages 589-605, November.
    61. Maurizio Bovi, 2003. "Nonparametric Analysis Of The International Business Cycles," ISAE Working Papers 37, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    62. Mirko Abbritti; Sebastian Weber, 2008. "Labor Market Rigidities and the Business Cycle: Price vs. Quantity Restricting Institutions," IHEID Working Papers 01-2008, Economics Section, The Graduate Institute of International Studies, revised Jan 2008.
    63. Luis J. Álvarez & Alberto Cabrero, 2010. "Does housing really lead the business cycle?," Working Papers 1024, Banco de España.
    64. Juergen Bierbaumer-Polly, 2012. "Regional and Sectoral Business Cycles - Key Features for the Austrian economy," EcoMod2012 4074, EcoMod.
    65. Holtemöller, Oliver (Ed.) & Rahn, Jörg (Ed.) & Stierle, Michael H. (Ed.), 2009. "Characteristics of Business Cycles: Have they Changed?," IWH-Sonderhefte 5/2009, Halle Institute for Economic Research (IWH).
    66. Mr. Andreas Billmeier, 2004. "Ghostbusting: Which Output Gap Measure Really Matters?," IMF Working Papers 2004/146, International Monetary Fund.
    67. Michael Artis, 2003. "Is there a European Business Cycle?," CESifo Working Paper Series 1053, CESifo.
    68. Klaus Abberger & Wolfgang Nierhaus, 2008. "Ifo capacity utilisation – a coincident indicator for business activity in German manufacturing," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 61(16), pages 15-23, August.
    69. Sanvi Avouyi-Dovi & Julien Matheron, 2005. "Interactions between business cycles, financial cycles and monetary policy: stylised facts," BIS Papers chapters, in: Bank for International Settlements (ed.), Investigating the relationship between the financial and real economy, volume 22, pages 273-98, Bank for International Settlements.
    70. Jacques Anas & Monica Billio & Laurent Ferrara & Gian Luigi Mazzi, 2008. "A System For Dating And Detecting Turning Points In The Euro Area," Manchester School, University of Manchester, vol. 76(5), pages 549-577, September.
    71. Mr. Paul Cashin, 2004. "Caribbean Business Cycles," IMF Working Papers 2004/136, International Monetary Fund.
    72. Poplawski Ribeiro, Marcos & Beetsma, Roel, 2008. "The political economy of structural reforms under a deficit restriction," Journal of Macroeconomics, Elsevier, vol. 30(1), pages 179-198, March.
    73. Pedro Cerqueira, 2011. "How Pervasive is the World Business Cycle?," Open Economies Review, Springer, vol. 22(1), pages 119-142, February.
    74. Monica Billio & Jacques Anas & Laurent Ferrara & Marco Lo Duca, 2007. "Business Cycle Analysis with Multivariate Markov Switching Models," Working Papers 2007_32, Department of Economics, University of Venice "Ca' Foscari".
    75. Andrew Hallett & Christian Richter, 2006. "Measuring the Degree of Convergence among European Business Cycles," Computational Economics, Springer;Society for Computational Economics, vol. 27(2), pages 229-259, May.
    76. Wolfgang Nierhaus & Klaus Abberger, 2014. "Forecasting business-cycle turning points: the three-times-in-succession rule vs. Markov switching," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 67(16), pages 21-25, August.
    77. Andrew Hughes Hallett & Christian R. Richter, 2007. "Time Varying Cyclical Analysis for Economies in Transition," CASE Network Studies and Analyses 0334, CASE-Center for Social and Economic Research.
    78. Charles Amélie & Darné Olivier & Claude Diebolt, 2011. "A Revision of the US Business-Cycles Chronology 1790–1928," Working Papers 11-01, Association Française de Cliométrie (AFC).
    79. Peter Martey Addo & Monica Billio & Dominique Guegan, 2012. "Studies in Nonlinear Dynamics and Wavelets for Business Cycle Analysis," Documents de travail du Centre d'Economie de la Sorbonne 12023r, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Nov 2013.
    80. Carlo Altavilla, 2004. "Do EMU Members Share the Same Business Cycle?," Journal of Common Market Studies, Wiley Blackwell, vol. 42(5), pages 869-896, December.
    81. Andrew Hughes Hallett & Christian Richter, 2009. "Is the US no longer the economy of first resort? Changing economic relationships in the Asia-Pacific region," International Economics and Economic Policy, Springer, vol. 6(2), pages 207-234, July.
    82. Stefan Sauer & Klaus Wohlrabe, 2020. "ifo Handbuch der Konjunkturumfragen," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 88.
    83. Luís Aguiar-Conraria & Manuel M. F. Martins & Maria Joana Soares, 2011. "Synchronization of Economic Sentiment Cycles in the Euro Area: a time-frequency analysis," CEF.UP Working Papers 1105, Universidade do Porto, Faculdade de Economia do Porto.
    84. Willem Boshoff, 2005. "The properties of cycles in South African financial variables and their relation to the business cycle," Working Papers 02/2005, Stellenbosch University, Department of Economics.
    85. Giannone, Domenico & Reichlin, Lucrezia & Lenza, Michele, 2009. "Business cycles in the euro area," Working Paper Series 1010, European Central Bank.
    86. Proietti, Tommaso, 2005. "New algorithms for dating the business cycle," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 477-498, April.
    87. Klaus Abberger & Wolfgang Nierhaus, 2011. "Current Economic Developments in View of the Ifo Economic Traffic Light," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 64(22), pages 36-38, November.

  121. Marcellino, Massimiliano, 2002. "Some Stylized Facts on Non-Systematic Fiscal Policy in the Euro Area," CEPR Discussion Papers 3635, C.E.P.R. Discussion Papers.

    Cited by:

    1. Salotti, Simone & Marattin, Luigi, 2009. "On the usefulness of government spending in the EU area," MPRA Paper 19476, University Library of Munich, Germany.
    2. Richard Kneller & Florian Misch, 2017. "A Survey On The Output Effects Of Tax Reforms From A Policy Perspective," Contemporary Economic Policy, Western Economic Association International, vol. 35(1), pages 165-192, January.
    3. Francisco de Castro, 2006. "The macroeconomic effects of fiscal policy in Spain," Applied Economics, Taylor & Francis Journals, vol. 38(8), pages 913-924.
    4. Ignacio Lozano & Karen Rodríguez, 2011. "Assessing the macroeconomic effects of fiscal policy in Colombia," Journal of Financial Economic Policy, Emerald Group Publishing Limited, vol. 3(3), pages 206-228, August.
    5. Ignacio Lozano Espitia & Karen Rodríguez, 2009. "Assessing the Macroeconomic Effects of Fiscal," Borradores de Economia 5386, Banco de la Republica.
    6. Enrico MARCHETTI & Francesco BUSATO & Bruno CHIARINI & Enrico MARCHETTI, 2010. "Indeterminacy, Underground Activities and Tax Evasion," EcoMod2010 259600112, EcoMod.
    7. Nilsson, Kristian, 2008. "Conceptual Framework for Fiscal Policy," Occasional Papers 16, National Institute of Economic Research.
    8. Hernández de Cos, Pablo & de Castro Fernández, Francisco, 2006. "The economic effects of exogenous fiscal shocks in Spain: a SVAR approach," Working Paper Series 647, European Central Bank.
    9. Backé, Peter, 2004. "Fiscal policy and inflation volatility," Working Paper Series 317, European Central Bank.
    10. Francisco Castro & Daniel Garrote, 2015. "The effects of fiscal shocks on the exchange rate in the EMU and differences with the USA," Empirical Economics, Springer, vol. 49(4), pages 1341-1365, December.
    11. Antonello D’Agostino & Jacopo Cimadomo, 2015. "Combining time-variation and mixed-frequencies: an analysis of government spending multipliers in Italy," Working Papers 7, European Stability Mechanism.
    12. L. Marattin & S. Salotti, 2009. "The Response of Private Consumption to Different Public Spending Categories: VAR Evidence from UK," Working Papers 670, Dipartimento Scienze Economiche, Universita' di Bologna.
    13. Jerome Henry & Pablo Hernandez de Cos & Sandro Momigliano, 2004. "The short-term impact of government budgets on prices; evidence from macroeconometric models," Temi di discussione (Economic working papers) 523, Bank of Italy, Economic Research and International Relations Area.
    14. Hernán Rincón & Diego Rodríguez & Jorge Toro & Santiago Téllez, 2014. "FISCO: Modelo Fiscal para Colombia," Borradores de Economia 855, Banco de la Republica de Colombia.
    15. Burriel, Pablo & de Castro Fernández, Francisco & Garrote, Daniel & Gordo, Esther & Paredes, Joan & Pérez, Javier J., 2009. "Fiscal policy shocks in the euro area and the US: an empirical assessment," Working Paper Series 1133, European Central Bank.
    16. Wolff, Guntram B. & Tenhofen, Jörn & Heppke-Falk, Kirsten H., 2006. "The macroeconomic effects of exogenous fiscal policy shocks in Germany: a disaggregated SVAR analysis," Discussion Paper Series 1: Economic Studies 2006,41, Deutsche Bundesbank.
    17. de Arcangelis, Giuseppe & Lamartina, Serena, 2003. "Identifying fiscal shocks and policy regimes in OECD countries," Working Paper Series 281, European Central Bank.
    18. Roel Beetsma & Massimo Giuliodori & Franc Klaassen, 2006. "Trade spill-overs of fiscal policy in the European Union: a panel analysis [‘Fiscal policy, profits, and investment’]," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 21(48), pages 640-687.
    19. Jérôme Creel & Eric Heyer & Mathieu Plane, 2011. "Petit précis de politique budgétaire par tous les temps," SciencePo Working papers Main hal-03460510, HAL.
    20. Nicholas Apergis & Arusha Cooray, 2013. "Forecasting fiscal variables: Only a strong growth plan can sustain the Greek austerity programs - Evidence from simultaneous and structural models," CAMA Working Papers 2013-25, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    21. Afonso, António & Claeys, Peter, 2007. "The dynamic behaviour of budget components and output," Working Paper Series 775, European Central Bank.
    22. Hollmayr, Josef & Kuckuck, Jan, 2018. "Fiscal multipliers of central, state and local government and of the social security funds in Germany: Evidence of a SVAR," Discussion Papers 28/2018, Deutsche Bundesbank.
    23. Leonel Muinelo-Gallo & Oriol Roca-Sagalés, 2017. "Long-term effects of fiscal policy in Uruguay," Documentos de Trabajo (working papers) 17-02, Instituto de Economía - IECON.
    24. Ana GRDOVIĆ GNIP, 2015. "Empirical Assessment Of Stabilization Effects Of Fiscal Policy In Croatia," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 47-69, March.
    25. Jacopo Cimadomo & Agnès Bénassy-Quéré, 2012. "Changing Patterns of Fiscal Policy Multipliers in Germany, the UK and the US," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00966144, HAL.
    26. Ana Mitreska & Sultanija Bojcheva – Terzijan, 2017. "Panel Estimation of the Impact of Foreign Banks Presence on Selected Banking Indicators in Macedonia," Working Papers 2017-04, National Bank of the Republic of North Macedonia.
    27. Daniela Iuliana Radu, 2015. "European System of Central Banks in Fiscal Policy Community," Knowledge Horizons - Economics, Faculty of Finance, Banking and Accountancy Bucharest,"Dimitrie Cantemir" Christian University Bucharest, vol. 7(2), pages 108-110, June.
    28. Carine Bouthevillain & John Caruana & Cristina Checherita & Jorge Cunha & Esther Gordo & Stephan Haroutunian & Amela Hubic & Geert Langenus & Bernhard Manzke & Javier J. Pérez & Pietro Tommasino, 2009. "Pros and Cons of various fiscal measures to stimulate the economy," BCL working papers 40, Central Bank of Luxembourg.
    29. Paredes, Joan & Pedregal, Diego J. & Pérez, Javier J., 2014. "Fiscal policy analysis in the euro area: Expanding the toolkit," Journal of Policy Modeling, Elsevier, vol. 36(5), pages 800-823.
    30. Oscar Bajo-Rubio & Burcu Berke, 2014. "Fiscal policy and the real exchange rate: Some evidence from Spain," Working Papers 14-11, Asociación Española de Economía y Finanzas Internacionales.
    31. Moumita Basu & Rilina Basu & Ranjanendra Narayan Nag, 2022. "A Dependent Economy Model of Employment, Real Exchange Rate and Debt Dynamics: Towards an Understanding of Pandemic Crisis," Foreign Trade Review, , vol. 57(1), pages 85-113, February.
    32. Jérôme Creel & Éric Heyer & Mathieu Plane, 2011. "Petit précis de politique budgétaire par tous les temps. Les multiplicateurs budgétaires au cours du cycle," Revue de l'OFCE, Presses de Sciences-Po, vol. 0(1), pages 61-88.
    33. Xavier Ramos & Oriol Roca-Sagales, 2007. "Long Term Effects of Fiscal Policy on the Size and the Distribution of the Pie in the UK," RSCAS Working Papers 2007/39, European University Institute.
    34. Rilind Kabashi, 2017. "Macroeconomic effects of fiscal policy in the European Union, with particular reference to transition countries," Public Sector Economics, Institute of Public Finance, vol. 41(1), pages 39-69.
    35. Thomas A. Alexopoulos & Henry Thompson, 2021. "A macroeconomic simulation for Greece in the wake of its government debt crisis," Economic Change and Restructuring, Springer, vol. 54(3), pages 699-716, August.
    36. de Castro, Francisco & Hernández de Cos, Pablo, 2008. "The economic effects of fiscal policy: The case of Spain," Journal of Macroeconomics, Elsevier, vol. 30(3), pages 1005-1028, September.
    37. Lorenzo Forni & Libero Monteforte & Luca Sessa, 2007. "The general equilibrium effects of fiscal policy: estimates for the euro area," Temi di discussione (Economic working papers) 652, Bank of Italy, Economic Research and International Relations Area.
    38. Hasko, Harri, 2007. "Some unpleasant fiscal arithmetic: the role of monetary and fiscal policy in public debt dynamics since the 1970s," Bank of Finland Research Discussion Papers 28/2007, Bank of Finland.
    39. Carriero, Andrea & Mumtaz, Haroon & Theophilopoulou, Angeliki, 2015. "Macroeconomic information, structural change, and the prediction of fiscal aggregates," International Journal of Forecasting, Elsevier, vol. 31(2), pages 325-348.
    40. Henry, Jérôme & Hernández de Cos, Pablo & Momigliano, Sandro, 2004. "The short-term impact of government budgets on prices: evidence from macroeconomic models," Working Paper Series 396, European Central Bank.
    41. Georgios Magkonis & Anastasia Theofilakou, 2019. "Transmission of sectoral debt shocks in OECD countries: Evidence from the income channel," Working Papers in Economics & Finance 2019-02, University of Portsmouth, Portsmouth Business School, Economics and Finance Subject Group.
    42. Alfredo Marvão Pereira & Oriol Roca‐Sagalés, 2011. "Long‐term effects of fiscal policies in Portugal," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 38(1), pages 114-127, January.
    43. Peter Claeys & Rosina Moreno & Jordi Suriñach, 2010. "Fiscal Policy and Interest Rates: The Role of Financial and Economic Integration," Advances in Spatial Science, in: Antonio Páez & Julie Gallo & Ron N. Buliung & Sandy Dall'erba (ed.), Progress in Spatial Analysis, pages 311-336, Springer.
    44. Francesco Caprioli & Sandro Momigliano, 2011. "The effects of fiscal shocks with debt-stabilizing budgetary policies in Italy," Temi di discussione (Economic working papers) 839, Bank of Italy, Economic Research and International Relations Area.
    45. Henry, Jerome & Hernandez de Cos, Pablo & Momigliano, Sandro, 2008. "The impact of government budgets on prices: Evidence from macroeconometric models," Journal of Policy Modeling, Elsevier, vol. 30(1), pages 123-143.
    46. Dragomirescu-Gaina, Catalin & Philippas, Dionisis, 2015. "Strategic interactions of fiscal policies in Europe: A global VAR perspective," Journal of International Money and Finance, Elsevier, vol. 59(C), pages 49-76.
    47. António Afonso & Peter Claeys, 2006. "The dynamic behaviour of budget components and output – the cases of France, Germany, Portugal, and Spain," Working Papers Department of Economics 2006/26, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    48. Elie Girard & Olivier Biau, 2005. "Politique budgétaire et dynamique économique en France : l'approche VAR structurel," Économie et Prévision, Programme National Persée, vol. 169(3), pages 1-23.
    49. Raffaella Basile & Bruno Chiarini & Elisabetta Marzano, 2011. "Can we Rely upon Fiscal Policy Estimates in Countries with Unreported Production of 15 Per Cent (or more) of GDP?," CESifo Working Paper Series 3521, CESifo.
    50. Massimo Giuliodori & Roel Beetsma, 2005. "What are the Trade Spill-Overs from Fiscal Shocks in Europe? An Empirical Analysis**," De Economist, Springer, vol. 153(2), pages 167-197, June.
    51. Toshihiro Ihori, 2013. "Fiscal Fluctuation Risks and Intergovernmental Functional Allocation," Public Policy Review, Policy Research Institute, Ministry of Finance Japan, vol. 9(1), pages 1-32, January.
    52. Bernd Hayo & Matthias Uhl, 2011. "The Effects of Legislated Tax Changes in Germany," MAGKS Papers on Economics 201142, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    53. Stephanos Papadamou & Trifon Tzivinikos, 2017. "The macroeconomic effects of fiscal consolidation policies in Greece," Journal of Financial Economic Policy, Emerald Group Publishing Limited, vol. 9(1), pages 34-49, April.
    54. Sébastien Pommier, 2008. "The Use of Fiscal Policy in EMU: First Appraisal and Future Prospects," EKONOMIAZ. Revista vasca de Economía, Gobierno Vasco / Eusko Jaurlaritza / Basque Government, vol. 69(03), pages 28-45.
    55. Bode, Oliver & Gerke, Rafael & Schellhorn, Hannes, 2006. "Die Wirkung fiskalischer Schocks auf das Bruttoinlandsprodukt," Working Papers 01/2006, German Council of Economic Experts / Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung.
    56. Luca, Pieroni & Lorusso, Marco, 2015. "Are all the fiscal policy shocks identical? Analysing the effects on private consumption of civilian and military spending shocks," MPRA Paper 69084, University Library of Munich, Germany.
    57. Matthias Uhl, 2013. "A History of Tax Legislation in the Federal Republic of Germany," MAGKS Papers on Economics 201311, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    58. Pieroni, Luca & Lorusso, Marco, 2013. "The Role of Fiscal Policy Components in Private Consumption: a Re-examination of the Effects of Military and Civilian Spending," MPRA Paper 47878, University Library of Munich, Germany.
    59. Michael W.M. Roos, 2007. "Die makroökonomischen Wirkungen diskretionärer Fiskalpolitik in Deutschland – Was wissen wir empirisch?," Perspektiven der Wirtschaftspolitik, Verein für Socialpolitik, vol. 8(4), pages 293-308, November.
    60. Monika Pécsyová, 2014. "Odhad vplyvu fiškálnej konsolidácie na rast HDP v SR [Estimated Impact of Fiscal Consolidation on GDP Growth in the Slovak Republic]," Politická ekonomie, Prague University of Economics and Business, vol. 2014(2), pages 174-193.
    61. Amela HUBIC & Francisco DE CASTRO, 2010. "The Effects of Inflation on General Government Accounts," EcoMod2010 259600077, EcoMod.

  122. Artis, Michael & Banerjee, Anindya & Marcellino, Massimiliano, 2002. "Factor Forecasts for the UK," CEPR Discussion Papers 3119, C.E.P.R. Discussion Papers.

    Cited by:

    1. Martin Schneider & Martin Spitzer, 2004. "Forecasting Austrian GDP using the generalized dynamic factor model," Working Papers 89, Oesterreichische Nationalbank (Austrian Central Bank).
    2. Eickmeier, Sandra, 2006. "Comovements and heterogeneity in the Comovements and heterogeneity in the dynamic factor model," Discussion Paper Series 1: Economic Studies 2006,31, Deutsche Bundesbank.
    3. T. Ando & R. S. Tsay, 2009. "‘Model selection for generalized linear models with factor‐augmented predictors’," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(3), pages 243-246, May.
    4. Hanan Naser, 2015. "Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods," Empirical Economics, Springer, vol. 49(2), pages 449-479, September.
    5. Eliana González & . Luis F. Melo & Viviana Monroy & Brayan Rojas, 2009. "A Dynamic Factor Model for the Colombian Inflation," Borradores de Economia 549, Banco de la Republica de Colombia.
    6. Erdemlioglu, Deniz, 2009. "Macro Factors in UK Excess Bond Returns: Principal Components and Factor-Model Approach," MPRA Paper 28895, University Library of Munich, Germany.
    7. Andrejs Bessonovs, 2015. "Suite of Latvia's GDP forecasting models," Working Papers 2015/01, Latvijas Banka.
    8. Anesti, Nikoleta & Galvao, Ana Beatriz & Miranda-Agrippino, Silvia, 2018. "Uncertain kingdom: nowcasting GDP and its revisions," LSE Research Online Documents on Economics 90382, London School of Economics and Political Science, LSE Library.
    9. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Working Papers 334, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    10. Curran, Declan & Funke, Michael, 2006. "Taking the temperature: forecasting GDP growth for mainland in China," BOFIT Discussion Papers 6/2006, Bank of Finland Institute for Emerging Economies (BOFIT).
    11. Troy D. Matheson, 2006. "Factor Model Forecasts for New Zealand," International Journal of Central Banking, International Journal of Central Banking, vol. 2(2), May.
    12. Siliverstovs Boriss & Kholodilin Konstantin A., 2012. "Assessing the Real-Time Informational Content of Macroeconomic Data Releases for Now-/Forecasting GDP: Evidence for Switzerland," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 232(4), pages 429-444, August.
    13. Eickmeier, Sandra & Breitung, Jörg, 2005. "How synchronized are central and east European economies with the euro area? Evidence from a structural factor model," Discussion Paper Series 1: Economic Studies 2005,20, Deutsche Bundesbank.
    14. Sandra Eickmeier & Joerg Breitung, 2006. "Business cycle transmission from the euro area to CEECs," Computing in Economics and Finance 2006 229, Society for Computational Economics.
    15. Ali Babikir & Henry Mwambi, 2016. "Evaluating the combined forecasts of the dynamic factor model and the artificial neural network model using linear and nonlinear combining methods," Empirical Economics, Springer, vol. 51(4), pages 1541-1556, December.
    16. Sonia de Lucas Santos & M. Jesús Delgado Rodríguez & Inmaculada Álvarez Ayuso & José Luis Cendejas Bueno, 2011. "Los ciclos económicos internacionales: antecedentes y revisión de la literatura," Cuadernos de Economía - Spanish Journal of Economics and Finance, Asociación Cuadernos de Economía, vol. 34(95), pages 73-84, Agosto.
    17. Andrea Cipollini & Nektarios Aslanidis, 2007. "Leading indicator properties of US high-yield credit spreads," Center for Economic Research (RECent) 006, University of Modena and Reggio E., Dept. of Economics "Marco Biagi".
    18. Massimo Guidolin & Stuart Hyde & David McMillan & Sadayuki Ono, 2010. "Does the macroeconomy predict U.K. asset returns in a nonlinear fashion? comprehensive out-of-sample evidence," Working Papers 2010-039, Federal Reserve Bank of St. Louis.
    19. Lombardi, Marco J. & Maier, Philipp, 2011. "Forecasting economic growth in the euro area during the Great Moderation and the Great Recession," Working Paper Series 1379, European Central Bank.
    20. Aleksandra Nocoń, 2020. "Sustainable Approach to the Normalization Process of the UK’s Monetary Policy," Sustainability, MDPI, vol. 12(21), pages 1-14, November.
    21. Erdinc Altay, 2003. "The Effect of Macroeconomic Factors on Asset Returns: A Comparative Analysis of the German and the Turkish Stock Markets in an APT Framework," Finance 0307006, University Library of Munich, Germany.
    22. Hwee Kwan Chow & Keen Meng Choy, 2009. "Analyzing and Forecasting Business Cycles in a Small Open Economy: A Dynamic Factor Model for Singapore," Working Papers 05-2009, Singapore Management University, School of Economics.
    23. Anindya Banerjee & Massimiliano Marcellino, 2003. "Are There Any Reliable Leading Indicators for U.S. Inflation and GDP Growth?," Working Papers 236, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    24. González-Rivera, Gloria & Ruiz Ortega, Esther & Maldonado, Javier, 2018. "Growth in Stress," DES - Working Papers. Statistics and Econometrics. WS 26623, Universidad Carlos III de Madrid. Departamento de Estadística.
    25. Christian M. Dahl & Henrik Hansen & John Smidt, 2008. "The cyclical component factor model," CREATES Research Papers 2008-44, Department of Economics and Business Economics, Aarhus University.
    26. Christian Gillitzer & Jonathan Kearns & Anthony Richards, 2005. "The Australian Business Cycle: A Coincident Indicator Approach," RBA Research Discussion Papers rdp2005-07, Reserve Bank of Australia.
    27. Matteo Luciani & Lorenzo Ricci, 2014. "Nowcasting Norway," International Journal of Central Banking, International Journal of Central Banking, vol. 10(4), pages 215-248, December.
    28. Kim, Hyun Hak & Swanson, Norman R., 2014. "Forecasting financial and macroeconomic variables using data reduction methods: New empirical evidence," Journal of Econometrics, Elsevier, vol. 178(P2), pages 352-367.
    29. Mark J. Holmes & Arthur Grimes, 2005. "Is there long-run convergence of regional house prices in the UK?," Working Papers 05_11, Motu Economic and Public Policy Research.
    30. Jörg Breitung & Sandra Eickmeier, 2006. "Dynamic factor models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 27-42, March.
    31. Eickmeier, Sandra & Breitung, Jorg, 2006. "How synchronized are new EU member states with the euro area? Evidence from a structural factor model," Journal of Comparative Economics, Elsevier, vol. 34(3), pages 538-563, September.
    32. Barhoumi, K. & Rünstler, G. & Cristadoro, R. & Den Reijer, A. & Jakaitiene, A. & Jelonek, P. & Rua, A. & Ruth, K. & Benk, S. & Van Nieuwenhuyze, C., 2008. "Short-term forecasting of GDP using large monthly datasets: a pseudo real-time forecast evaluation exercise," Working papers 215, Banque de France.
    33. Massimiliano Serati & Matteo Manera & Michele Plotegher, 2008. "Modelling electricity prices: from the state of the art to a draft of a new proposal," LIUC Papers in Economics 210, Cattaneo University (LIUC).
    34. Oguzhan Cepni & I. Ethem Guney & Norman R. Swanson, 2020. "Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 18-36, January.
    35. Heather D. Gibson & Stephen G. Hall & George S. Tavlas, 2020. "A Suggestion for a Dynamic Multi Factor Model (DMFM)," Working Papers 282, Bank of Greece.
    36. Poghosyan, K., 2012. "Structural and reduced-form modeling and forecasting with application to Armenia," Other publications TiSEM ad1a24c3-15e6-4f04-b338-3, Tilburg University, School of Economics and Management.
    37. António Rua & Francisco Craveiro Dias, 2008. "Forecasting Using Targeted Diffusion Indexes," Working Papers w200807, Banco de Portugal, Economics and Research Department.
    38. Poghosyan, Karen & Poghosyan, Ruben, 2021. "On the applicability of dynamic factor models for forecasting real GDP growth in Armenia," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 61, pages 28-46.
    39. Smeekes, Stephan & Wijler, Etiënne, 2016. "Macroeconomic Forecasting Using Penalized Regression Methods," Research Memorandum 039, Maastricht University, Graduate School of Business and Economics (GSBE).
    40. Hyun Hak Kim & Norman Swanson, 2013. "Mining Big Data Using Parsimonious Factor and Shrinkage Methods," Departmental Working Papers 201316, Rutgers University, Department of Economics.
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    42. Anton Grui & Roman Lysenko, 2017. "Nowcasting Ukraine's GDP Using a Factor-Augmented VAR (FAVAR) Model," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 242, pages 5-13.
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    44. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 4, pages 135-196, Elsevier.
    45. Bodnar, Taras & Reiß, Markus, 2016. "Exact and asymptotic tests on a factor model in low and large dimensions with applications," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 125-151.
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    60. Banbura, Marta & Rünstler, Gerhard, 2011. "A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP," International Journal of Forecasting, Elsevier, vol. 27(2), pages 333-346, April.
    61. Maehashi, Kohei & Shintani, Mototsugu, 2020. "Macroeconomic forecasting using factor models and machine learning: an application to Japan," Journal of the Japanese and International Economies, Elsevier, vol. 58(C).
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    63. Arvid Raknerud & Terje Skjerpen & Anders Rygh Swensen, 2007. "Forecasting key macroeconomic variables from a large number of predictors: A state space approach," Discussion Papers 504, Statistics Norway, Research Department.
    64. Johannes Tang Kristensen, 2012. "Factor-Based Forecasting in the Presence of Outliers: Are Factors Better Selected and Estimated by the Median than by The Mean?," CREATES Research Papers 2012-28, Department of Economics and Business Economics, Aarhus University.
    65. Álvaro Aguirre R. & Luis Felipe Céspedes C., 2004. "Uso de Análisis Factorial Dinámico para Proyecciones Macroeconómicas," Working Papers Central Bank of Chile 274, Central Bank of Chile.
    66. Boriss Siliverstovs & Konstantin A. Kholodilin, 2006. "On Selection of Components for a Diffusion Index Model: It's not the Size, It's How You Use It," Discussion Papers of DIW Berlin 598, DIW Berlin, German Institute for Economic Research.
    67. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2005. "Leading Indicators for Euro‐area Inflation and GDP Growth," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 785-813, December.
    68. In Choi & Dukpa Kim & Yun Jung Kim & Noh-Sun Kwark, 2016. "A Multilevel Factor Model: Identification, Asymptotic Theory and Applications," Working Papers 1609, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    69. Kohei Maehashi & Mototsugu Shintani, 2020. "Macroeconomic Forecasting Using Factor Models and Machine Learning: An Application to Japan," CIRJE F-Series CIRJE-F-1146, CIRJE, Faculty of Economics, University of Tokyo.
    70. Bruneau, C. & De Bandt, O. & Flageollet, A., 2003. "Forecasting Inflation in the Euro Area," Working papers 102, Banque de France.
    71. Zaher, Fadi, 2007. "Evaluating factor forecasts for the UK: The role of asset prices," International Journal of Forecasting, Elsevier, vol. 23(4), pages 679-693.
    72. Michael Bleaney & Paul Mizen & Veronica Veleanu, 2012. "Bond Spreads as Predictors of Economic Activity in Eight European Economies," Discussion Papers 12/11, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    73. Calista Cheung & Frédérick Demers, 2007. "Evaluating Forecasts from Factor Models for Canadian GDP Growth and Core Inflation," Staff Working Papers 07-8, Bank of Canada.
    74. In Choi, 2012. "Model Selection for Factor Analysis: Some New Criteria and Performance Comparisons," Working Papers 1209, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    75. G. Rünstler & K. Barhoumi & S. Benk & R. Cristadoro & A. Den Reijer & A. Jakaitiene & P. Jelonek & A. Rua & K. Ruth & C. Van Nieuwenhuyze, 2009. "Short-term forecasting of GDP using large datasets: a pseudo real-time forecast evaluation exercise," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 595-611.
    76. Christian Schumacher, 2007. "Forecasting German GDP using alternative factor models based on large datasets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 271-302.
    77. In Choi & Hanbat Jeong, 2020. "Differencing versus nondifferencing in factor‐based forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 728-750, September.
    78. In Choi, 2011. "Efficient Estimation of Nonstationary Factor Models," Working Papers 1101, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy), revised Jun 2011.
    79. António Rua, 2016. "A wavelet-based multivariate multiscale approach for forecasting," Working Papers w201612, Banco de Portugal, Economics and Research Department.
    80. Monica Defend & Aleksey Min & Lorenzo Portelli & Franz Ramsauer & Francesco Sandrini & Rudi Zagst, 2021. "Quantifying Drivers of Forecasted Returns Using Approximate Dynamic Factor Models for Mixed-Frequency Panel Data," Forecasting, MDPI, vol. 3(1), pages 1-35, February.
    81. Chantziara, Thalia & Skiadopoulos, George, 2008. "Can the dynamics of the term structure of petroleum futures be forecasted? Evidence from major markets," Energy Economics, Elsevier, vol. 30(3), pages 962-985, May.
    82. Bai, Jushan, 2004. "Estimating cross-section common stochastic trends in nonstationary panel data," Journal of Econometrics, Elsevier, vol. 122(1), pages 137-183, September.
    83. Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
    84. Christian Schulz, 2007. "Forecasting economic growth for Estonia : application of common factor methodologies," Bank of Estonia Working Papers 2007-09, Bank of Estonia, revised 04 Sep 2007.
    85. Nikoleta Anesti & Ana Beatriz Galvão & Silvia Miranda‐Agrippino, 2022. "Uncertain Kingdom: Nowcasting Gross Domestic Product and its revisions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 42-62, January.
    86. Michael Bleaney & Paul Mizen & Veronica Veleanu, 2013. "Bond Spreads and Economic Activity in Eight European Economies," Discussion Papers 2013/09, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    87. Santos, Sonia de Lucas & Rodríguez, María Jesús Delgado & Ayuso, Inmaculada Álvarez, 2011. "Application of factor models for the identification of countries sharing international reference-cycles," Economic Modelling, Elsevier, vol. 28(6), pages 2424-2431.
    88. Dimitar EFTIMOSKI, 2019. "Improving Short-Term Forecasting of Macedonian GDP: Comparing the Factor Model with the Macroeconomic Structural Equation Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 32-53, June.
    89. Schumacher Christian & Dreger Christian, 2004. "Estimating Large-Scale Factor Models for Economic Activity in Germany: Do They Outperform Simpler Models? / Die Schätzung von großen Faktormodellen für die deutsche Volkswirtschaft: Übertreffen sie ei," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 224(6), pages 731-750, December.
    90. Jennifer Castle & David Hendry & Oleg Kitov, 2013. "Forecasting and Nowcasting Macroeconomic Variables: A Methodological Overview," Economics Series Working Papers 674, University of Oxford, Department of Economics.
    91. Chris Heaton & Natalia Ponomareva & Qin Zhang, 2020. "Forecasting models for the Chinese macroeconomy: the simpler the better?," Empirical Economics, Springer, vol. 58(1), pages 139-167, January.
    92. Hallin, Marc & Liska, Roman, 2011. "Dynamic factors in the presence of blocks," Journal of Econometrics, Elsevier, vol. 163(1), pages 29-41, July.
    93. Eickmeier, Sandra, 2005. "Common stationary and non-stationary factors in the euro area analyzed in a large-scale factor model," Discussion Paper Series 1: Economic Studies 2005,02, Deutsche Bundesbank.
    94. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    95. Álvaro Aguirre R. & Luis Felipe Céspedes C., 2004. "Use of Dynamic Factor Analysis in Macroeconomic Forecasts," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 7(3), pages 35-46, December.

  123. Angelini, Henry, Marcellino, 2002. "interpolation with a large information set," Computing in Economics and Finance 2002 72, Society for Computational Economics.

    Cited by:

    1. Massimiliano Marcellino, 2007. "Pooling‐Based Data Interpolation and Backdating," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(1), pages 53-71, January.
    2. Carlo Ambrogio Favero & Massimilano Marcellino & Francesca Neglia, "undated". "Principal components at work: The empirical analysis of monetary policy with large datasets," Working Papers 223, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    3. Angelini, Elena & Marcellino, Massimiliano, 2007. "Econometric analyses with backdated data: unified Germany and the euro area," Working Paper Series 752, European Central Bank.

  124. Marcellino, Massimiliano, 2002. "Forecasting EMU Macroeconomic Variables," CEPR Discussion Papers 3529, C.E.P.R. Discussion Papers.

    Cited by:

    1. Emma SARNO & Alberto ZAZZARO, 2003. "Structural Convergence of Macroeconomic Time Series: Evidence for Inflation Rates in EU Countries," Working Papers 180, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    2. Marcellino, Massimiliano, 2002. "Forecasting EMU Macroeconomic Variables," CEPR Discussion Papers 3529, C.E.P.R. Discussion Papers.
    3. Massimiliano Marcellino, 2008. "A linear benchmark for forecasting GDP growth and inflation?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 305-340.
    4. Ralf Brüggemann & Helmut Lütkepohl & Massimiliano Marcellino, 2008. "Forecasting euro area variables with German pre-EMU data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(6), pages 465-481.
    5. Bekiros, Stelios & Cardani, Roberta & Paccagnini, Alessia & Villa, Stefania, 2016. "Dealing with financial instability under a DSGE modeling approach with banking intermediation: A predictability analysis versus TVP-VARs," Journal of Financial Stability, Elsevier, vol. 26(C), pages 216-227.
    6. Bekiros Stelios & Paccagnini Alessia, 2015. "Estimating point and density forecasts for the US economy with a factor-augmented vector autoregressive DSGE model," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(2), pages 107-136, April.
    7. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    8. Jing Zeng, 2015. "Combining Country-Specific Forecasts when Forecasting Euro Area Macroeconomic Aggregates," Working Paper Series of the Department of Economics, University of Konstanz 2015-11, Department of Economics, University of Konstanz.
    9. Stelios D. Bekiros & Alessia Paccagnini, 2014. "Bayesian forecasting with small and medium scale factor-augmented vector autoregressive DSGE models," Open Access publications 10197/7322, School of Economics, University College Dublin.
    10. Stelios D. Bekiros & Alessia Paccagnini, 2013. "On the predictability of time-varying VAR and DSGE models," Open Access publications 10197/7329, School of Economics, University College Dublin.
    11. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 8, pages 413-457, Elsevier.
    12. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2011. "Forecasting Nevada Gross Gaming Revenue and Taxable Sales Using Coincident and Leading Employment Indexes," Working Papers 1103, University of Nevada, Las Vegas , Department of Economics.
    13. Michael P. Clements & Philip Hans Franses & Norman R. Swanson, 2003. "Forecasting economic and financial time-series with non-linear models," Departmental Working Papers 200309, Rutgers University, Department of Economics.
    14. Kitov, Ivan, 2007. "Inflation, unemployment, labor force change in European countries," MPRA Paper 14557, University Library of Munich, Germany.
    15. Hubrich, Kirstin, 2005. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," International Journal of Forecasting, Elsevier, vol. 21(1), pages 119-136.
    16. Siliverstovs, B. & van Dijk, D.J.C., 2003. "Forecasting industrial production with linear, nonlinear, and structural change models," Econometric Institute Research Papers EI 2003-16, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    17. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    18. Carlo Favero & Massimiliano Marcellino, 2005. "Modelling and Forecasting Fiscal Variables for the Euro Area," Working Papers 298, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    19. Paweł Baranowski, 2008. "Reguła Taylora i jej rozszerzenia," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 7-8, pages 1-23.
    20. Stelios D. Bekiros & Alessia Paccagnini, 2014. "Policy-oriented macroeconomic forecasting with hybrid DGSE and time-varying parameter VAR models," Working Papers 2014-426, Department of Research, Ipag Business School.
    21. Stelios D. Bekiros & Alessia Paccagnini, 2015. "Macroprudential policy and forecasting using Hybrid DSGE models with financial frictions and State space Markov-Switching TVP-VARs," Open Access publications 10197/7333, School of Economics, University College Dublin.
    22. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    23. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working Papers 201230, University of Pretoria, Department of Economics.
    24. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2005. "Leading Indicators for Euro‐area Inflation and GDP Growth," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 785-813, December.
    25. Anindya Banerjee & Bill Russell, 2006. "A markup model for forecasting inflation for the euro area," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(7), pages 495-511.
    26. Anders Bredahl Kock & Timo Teräsvirta, 2010. "Forecasting with nonlinear time series models," CREATES Research Papers 2010-01, Department of Economics and Business Economics, Aarhus University.
    27. Mihaela Simionescu, 2014. "What Type Of Social Capital Is Engaged By The French Dairy Stockbreeders? A Characterization Through Their Professional Identities," Romanian Journal of Regional Science, Romanian Regional Science Association, vol. 8(1), pages 87-102, JUNE.
    28. Bo Zhang & Joshua C.C. Chan & Jamie L. Cross, 2018. "Stochastic volatility models with ARMA innovations: An application to G7 inflation forecasts," CAMA Working Papers 2018-32, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    29. Kauppi, Heikki & Virtanen, Timo, 2021. "Boosting nonlinear predictability of macroeconomic time series," International Journal of Forecasting, Elsevier, vol. 37(1), pages 151-170.
    30. Blerina Vika & Ilir Vika, 2021. "Forecasting Albanian Time Series with Linear and Nonlinear Univariate Models," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 10, September.
    31. Heikki Kauppi & Timo Virtanen, 2018. "Boosting Non-linear Predictabilityof Macroeconomic Time Series," Discussion Papers 124, Aboa Centre for Economics.
    32. Ralf Brüggemann & Jing Zeng, 2015. "Forecasting Euro-Area Macroeconomic Variables Using a Factor Model Approach for Backdating," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(1), pages 22-39, February.
    33. Amendola, Alessandra & Christian, Francq, 2009. "Concepts and tools for nonlinear time series modelling," MPRA Paper 15140, University Library of Munich, Germany.
    34. Enders, Walter & Pascalau, Razvan, 2015. "Pretesting for multi-step-ahead exchange rate forecasts with STAR models," International Journal of Forecasting, Elsevier, vol. 31(2), pages 473-487.
    35. Shahid IQBAL & Maqbool H. SIAL, 2016. "Projections of Inflation Dynamics for Pakistan: GMDH Approach," Journal of Economics and Political Economy, KSP Journals, vol. 3(3), pages 536-559, September.
    36. Jing Zeng, 2016. "Combining country-specific forecasts when forecasting Euro area macroeconomic aggregates," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 43(2), pages 415-444, May.
    37. Christos Avdoulas & Stelios Bekiros, 2018. "Nonlinear Forecasting of Euro Area Industrial Production Using Evolutionary Approaches," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 521-530, August.

  125. Banerjee, Anindya & Massimiliano Marcellino & Chiara Osbat, 2002. "Testing for PPP: Should We Use Panel Methods?," Royal Economic Society Annual Conference 2002 13, Royal Economic Society.

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    1. Christian Dreger & Hans-Eggert Reimers, 2006. "Hysteresis and Persistence in the Course of Unemployment: The EU and US Experience," Discussion Papers of DIW Berlin 572, DIW Berlin, German Institute for Economic Research.
    2. Marra, Alessandro & Colantonio, Emiliano, 2021. "The path to renewable energy consumption in the European Union through drivers and barriers: A panel vector autoregressive approach," Socio-Economic Planning Sciences, Elsevier, vol. 76(C).
    3. Belke, Ansgar & Dreger, Christian, 2011. "Current Account Imbalances in the Euro Area: Catching up or Competitiveness?," Ruhr Economic Papers 241, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    4. Carl S. Bonham & Peter Fuleky & Qianxue Zhao, 2013. "Estimating Demand Elasticities in Non-Stationary Panels: The Case of Hawaii's Tourism Industry," Working Papers 201303, University of Hawaii at Manoa, Department of Economics.
    5. Dang, Vinh Q.T. & So, Erin P.K. & Yang, Alan Yu & Chan, Kenneth S., 2020. "China and international market integration: Evidence from the law of one price in the Middle East and Africa," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    6. Christophe Hurlin & Valérie Mignon, 2006. "Une synthèse des tests de cointégration sur données de panel," Working Papers halshs-00070887, HAL.
    7. Kaddour Hadri & Eiji Kurozumi & Yao Rao, 2015. "Novel panel cointegration tests emending for cross‐section dependence with N fixed," Econometrics Journal, Royal Economic Society, vol. 18(3), pages 363-411, October.
    8. Amélie Charles & Olivier Darné & Jean-François Hoarau, 2010. "Does the real GDP per capita convergence hold in the Common Market for Eastern and Southern Africa?," Post-Print hal-00797485, HAL.
    9. Amornthum, Somchai & Bonham, Carl S., 2011. "Financial integration in the pacific basin region: RIP by PANIC attack?," Journal of International Money and Finance, Elsevier, vol. 30(6), pages 1019-1033, October.
    10. Everaert, Gerdie, 2014. "A panel analysis of the fisher effect with an unobserved I(1) world real interest rate," Economic Modelling, Elsevier, vol. 41(C), pages 198-210.
    11. Pedro M. G. Martins, 2010. "Aid Absorption and Spending in Africa: A Panel Cointegration Approach," Working Paper Series 1010, Department of Economics, University of Sussex Business School.
    12. Kappler, Marcus, 2006. "Panel Tests for Unit Roots in Hours Worked," ZEW Discussion Papers 06-022, ZEW - Leibniz Centre for European Economic Research.
    13. Hu, Yang & Valera, Harold Glenn A. & Oxley, Les, 2019. "Market efficiency of the top market-cap cryptocurrencies: Further evidence from a panel framework," Finance Research Letters, Elsevier, vol. 31(C), pages 138-145.
    14. Matteo Lanzafame, 2014. "Current account sustainability in advanced economies," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 23(7), pages 1000-1017, October.
    15. Imed Drine & Christophe Rault, 2008. "Purchasing Power Parity for Developing and Developed Countries. What can we Learn from Non-Stationary Panel Data Models?," CESifo Working Paper Series 2255, CESifo.
    16. Saadet Kasman & Adnan Kasman & Duygu Ayhan, 2010. "Testing the Purchasing Power Parity Hypothesis for the New Member and Candidate Countries of the European Union: Evidence from Lagrange Multiplier Unit Root Tests with Structural Breaks," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 46(2), pages 53-65, March.
    17. Hubrich, Kirstin & Marcellino, Massimiliano & Beck, Günter W., 2006. "Regional inflation dynamics within and across euro area countries and a comparison with the US," Working Paper Series 681, European Central Bank.
    18. Syed A. Basher & Josep Lluis Carrión-i-Silvestre, 2008. "Deconstructing Shocks and Persistence in OECD Real Exchange Rates," Working Papers XREAP2008-06, Xarxa de Referència en Economia Aplicada (XREAP), revised Jun 2008.
    19. Tolga Omay & Mübariz Hasanov & Yongcheol Shin, 2018. "Testing for Unit Roots in Dynamic Panels with Smooth Breaks and Cross-Sectionally Dependent Errors," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 167-193, June.
    20. Omri, Anis & Daly, Saida & Rault, Christophe & Chaibi, Anissa, 2015. "Financial development, environmental quality, trade and economic growth: What causes what in MENA countries," Energy Economics, Elsevier, vol. 48(C), pages 242-252.
    21. Jorg Breitung & Gianluca Cubadda, 2009. "Testing for cointegration in high-dimensional systems," CEIS Research Paper 148, Tor Vergata University, CEIS, revised 30 Sep 2009.
    22. Karolina Konopczak & Andrzej Torój, 2010. "Estimating the Baumol-Bowen and Balassa-Samuelson Effects in the Polish Economy - a Disaggregated Approach," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 2(2), pages 117-150, March.
    23. Joakim, Westerlund & Johan, Blomquist, 2009. "Are Crime Rates Really Stationary?," Working Papers 2009:20, Lund University, Department of Economics.
    24. António AFONSO & Christophe RAULT, 2008. "What do we Really Know About Fiscal Sustainability in the EU? A Panel Data Diagnostic," EcoMod2008 23800000, EcoMod.
    25. Wagner, Martin, 2005. "On PPP, Unit Roots and Panels," Economics Series 176, Institute for Advanced Studies.
    26. Marcus Kappler, 2009. "Do hours worked contain a unit root? Evidence from panel data," Empirical Economics, Springer, vol. 36(3), pages 531-555, June.
    27. Mathilde Aubry & Jean Bonnet & Patricia Renou-Maissant, 2015. "Entrepreneurship and the business cycle: the “Schumpeter” effect versus the “refugee” effect—a French appraisal based on regional data," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 54(1), pages 23-55, January.
    28. Dreger, Christian, 2010. "Does the nominal exchange rate regime affect the real interest parity condition?," The North American Journal of Economics and Finance, Elsevier, vol. 21(3), pages 274-285, December.
    29. Bhavesh Garg & Pravakar Sahoo, 2021. "DO DIFFERENT TYPES OF CAPITAL INFLOWS HAVE DIFFERENTIAL IMPACT ON OUTPUT? Evidence from Time series and Panel Analysis," IEG Working Papers 443, Institute of Economic Growth.
    30. Jushan Bai & Serena Ng, 2001. "A New Look at Panel Testing of Stationarity and the PPP Hypothesis," Economics Working Paper Archive 467, The Johns Hopkins University,Department of Economics.
    31. Das, Samarjit & Bhattacharya, Kaushik, 2004. "Price Convergence across Regions in India," Bonn Econ Discussion Papers 1/2005, University of Bonn, Bonn Graduate School of Economics (BGSE).
    32. Mariam Camarero & Josep Lluis Carrion-i-Silvestre & Cecilio Tamarit, 2006. "New evidence of the real interest rate parity for OECD countries using panel unit root tests with breaks," Working Papers CREAP2006-14, Xarxa de Referència en Economia Aplicada (XREAP), revised Dec 2006.
    33. Alfonso ARPAIA & Alessandro TURRINI, 2008. "Government Expenditure and Economic Growth in the EU: Long-Run Tendencies and Short-Term Adjustment," EcoMod2008 23800006, EcoMod.
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    1. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2015. "Was the recent downturn in US real GDP predictable?," Applied Economics, Taylor & Francis Journals, vol. 47(28), pages 2985-3007, June.
    2. Imad Moosa & John Vaz, 2015. "Directional accuracy, forecasting error and the profitability of currency trading: model-based evidence," Applied Economics, Taylor & Francis Journals, vol. 47(57), pages 6191-6199, December.
    3. Marcellino, Massimiliano, 2002. "Forecasting EMU Macroeconomic Variables," CEPR Discussion Papers 3529, C.E.P.R. Discussion Papers.
    4. D van Dijk & D R Osborn & M Sensier, 2002. "Changes in Variability of the Business Cycle in the G7 Countries," Centre for Growth and Business Cycle Research Discussion Paper Series 16, Economics, The University of Manchester.
    5. Ferrara, Laurent & Marcellino, Massimiliano & Mogliani, Matteo, 2015. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," International Journal of Forecasting, Elsevier, vol. 31(3), pages 664-679.
    6. Terasvirta, Timo & van Dijk, Dick & Medeiros, Marcelo C., 2005. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," International Journal of Forecasting, Elsevier, vol. 21(4), pages 755-774.
    7. Michael W. McCracken & Todd E. Clark, 2003. "The Predictive Content of the Output Gap for Inflation: Resolving In-Sample and Out-of-Sample Evidence," Computing in Economics and Finance 2003 183, Society for Computational Economics.
    8. Anders Bredahl Kock & Timo Teräsvirta, 2016. "Forecasting Macroeconomic Variables Using Neural Network Models and Three Automated Model Selection Techniques," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1753-1779, December.
    9. Costas Milas & Philip Rothman, 2007. "Out-of-Sample Forecasting of Unemployment Rates with Pooled STVECM Forecasts," Working Paper series 49_07, Rimini Centre for Economic Analysis.
    10. Habrov, Vladimir, 2012. "Optimization of portfolio management based on vector autoregression models and multivariate volatility models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 28(4), pages 35-62.
    11. Todd E. Clark & Michael W. McCracken, 2002. "Forecast-based model selection in the presence of structural breaks," Research Working Paper RWP 02-05, Federal Reserve Bank of Kansas City.
    12. Terasvirta, Timo, 2006. "Forecasting economic variables with nonlinear models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 8, pages 413-457, Elsevier.
    13. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2011. "Forecasting Nevada Gross Gaming Revenue and Taxable Sales Using Coincident and Leading Employment Indexes," Working Papers 1103, University of Nevada, Las Vegas , Department of Economics.
    14. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
    15. Todd E. Clark & Michael W. McCracken, 2001. "Evaluating long-horizon forecasts," Research Working Paper RWP 01-14, Federal Reserve Bank of Kansas City.
    16. Siliverstovs, B. & van Dijk, D.J.C., 2003. "Forecasting industrial production with linear, nonlinear, and structural change models," Econometric Institute Research Papers EI 2003-16, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    17. Carlo Favero & Massimiliano Marcellino, 2005. "Modelling and Forecasting Fiscal Variables for the Euro Area," Working Papers 298, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    18. Clark, Todd E. & McCracken, Michael W., 2005. "The power of tests of predictive ability in the presence of structural breaks," Journal of Econometrics, Elsevier, vol. 124(1), pages 1-31, January.
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    21. Ullrich Heilemann & Herman Stekler, 2010. "Perspectives on Evaluating Macroeconomic Forecasts," Working Papers 2010-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    22. Costas Milas & Phil Rothman, 2005. "Multivariate STAR Unemployment Rate Forecasts," Econometrics 0502010, University Library of Munich, Germany.
    23. Massimiliano Marcellino, "undated". "Forecast pooling for short time series of macroeconomic variables," Working Papers 212, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    24. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working Papers 201230, University of Pretoria, Department of Economics.
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    1. Lamprou, Dimitra, 2016. "Nowcasting GDP in Greece: The impact of data revisions and forecast origin on model selection and performance," The Journal of Economic Asymmetries, Elsevier, vol. 14(PA), pages 93-102.
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    6. Kajal Lahiri & Xuguang Sheng, 2008. "Measuring Forecast Uncertainty by Disagreement: The Missing Link," ifo Working Paper Series 60, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    7. Mikosch, Heiner & Solanko, Laura, 2017. "Should one follow movements in the oil price or in money supply? Forecasting quarterly GDP growth in Russia with higher-frequency indicators," BOFIT Discussion Papers 19/2017, Bank of Finland Institute for Emerging Economies (BOFIT).
    8. Heij, Christiaan & van Dijk, Dick & Groenen, Patrick J.F., 2011. "Real-time macroeconomic forecasting with leading indicators: An empirical comparison," International Journal of Forecasting, Elsevier, vol. 27(2), pages 466-481, April.
    9. Alonso Gomez & John M Maheu & Alex Maynard, 2008. "Improving Forecasts of Inflation using the Term Structure of Interest Rates," Working Papers tecipa-319, University of Toronto, Department of Economics.
    10. Kholodilin Konstantin Arkadievich & Siliverstovs Boriss, 2006. "On the Forecasting Properties of the Alternative Leading Indicators for the German GDP: Recent Evidence," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 226(3), pages 234-259, June.
    11. Massimiliano Marcellino, 2008. "A linear benchmark for forecasting GDP growth and inflation?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 305-340.
    12. Lahiri, Kajal & Sheng, Xuguang, 2010. "Learning and heterogeneity in GDP and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 26(2), pages 265-292, April.
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    15. Ciccarelli, Matteo & Mojon, Benoît, 2006. "Global Inflation," Kiel Working Papers 1337, Kiel Institute for the World Economy (IfW Kiel).
    16. Knut Are Aastveit & Karsten R. Gerdrup & Anne Sofie Jore & Leif Anders Thorsrud, 2011. "Nowcasting GDP in real-time: A density combination approach," Working Paper 2011/11, Norges Bank.
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    19. Chee Yin Yip & Hock Eam Lim & Hooi Hooi Lean, 2016. "Effectiveness of a Cluster of Determinants to Increase Economic Growth Rate: A Combined Statistical Criteria Approach," International Journal of Economics and Financial Issues, Econjournals, vol. 6(2), pages 728-735.
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    21. Garnitz, Johanna & Lehmann, Robert & Wohlrabe, Klaus, 2019. "Forecasting GDP all over the world using leading indicators based on comprehensive survey data," Munich Reprints in Economics 78264, University of Munich, Department of Economics.
    22. Heij, Christiaan & van Dijk, Dick & Groenen, Patrick J.F., 2008. "Macroeconomic forecasting with matched principal components," International Journal of Forecasting, Elsevier, vol. 24(1), pages 87-100.
    23. Heij, C. & van Dijk, D.J.C. & Groenen, P.J.F., 2009. "Macroeconomic forecasting with real-time data: an empirical comparison," Econometric Institute Research Papers EI 2009-27, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    24. Jason Angelopoulos, 2017. "Creating and assessing composite indicators: Dynamic applications for the port industry and seaborne trade," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(1), pages 126-159, March.
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    30. Haroon Khan, 2015. "The Impact of Oil and Gold Prices on the GDP Growth: Empirical Evidence from a Developing Country," International Journal of Management Science and Business Administration, Inovatus Services Ltd., vol. 1(11), pages 34-46, October.
    31. Oguzhan Cepni & I. Ethem Guney & Norman R. Swanson, 2020. "Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 18-36, January.
    32. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "Pooling versus model selection for nowcasting with many predictors: an application to German GDP," Discussion Paper Series 1: Economic Studies 2009,03, Deutsche Bundesbank.
    33. Radoslaw Sobko & Maria Klonowska-Matynia, 2021. "The Relationship between the Purchasing Managers’ Index (PMI) and Economic Growth: The Case for Poland," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 1), pages 198-219.
    34. Scharnagl, Michael & Schumacher, Christian, 2007. "Reconsidering the role of monetary indicators for euro area inflation from a Bayesian perspective using group inclusion probabilities," Discussion Paper Series 1: Economic Studies 2007,09, Deutsche Bundesbank.
    35. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    36. Milena Lipovina-Božović, 2013. "A Comparison Of The Var Model And The Pc Factor Model In Forecasting Inflation In Montenegro," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 58(198), pages 115-136, July - Se.
    37. Bornali Bhandari & Samarth Gupta & Ajaya K. Sahu & K. S. Urs, 2021. "Business sentiments during India’s national lockdown: Lessons for second and potential third wave," Indian Economic Review, Springer, vol. 56(2), pages 335-350, December.
    38. Diron, Marie & Mojon, Benoît, 2005. "Forecasting the central bank's inflation objective is a good rule of thumb," Working Paper Series 564, European Central Bank.
    39. George Bagdatoglou & Alexandros Kontonikas & Mark E. Wohar, 2016. "Forecasting Us Inflation Using Dynamic General-To-Specific Model Selection," Bulletin of Economic Research, Wiley Blackwell, vol. 68(2), pages 151-167, April.
    40. Juan Laborda & Sonia Ruano & Ignacio Zamanillo, 2023. "Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
    41. Giovanni Cicceri & Giuseppe Inserra & Michele Limosani, 2020. "A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study," Mathematics, MDPI, vol. 8(2), pages 1-20, February.
    42. James H. Stock & Mark W. Watson, 2008. "Phillips Curve Inflation Forecasts," NBER Working Papers 14322, National Bureau of Economic Research, Inc.
    43. Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting the French index of industrial production: A comparison from bridge and factor models," Economic Modelling, Elsevier, vol. 29(6), pages 2174-2182.
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    45. Khundrakpam, Jeevan Kumar & George, Asish Thomas, 2012. "An Empirical Analysis of the Relationship between WPI and PMI-Manufacturing Price Indices in India," MPRA Paper 50929, University Library of Munich, Germany.
    46. Liu, Ping & James Hueng, C., 2017. "Measuring real business condition in China," China Economic Review, Elsevier, vol. 46(C), pages 261-274.
    47. Massimiliano Marcellino & Christian Schumacher, 2008. "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP," Economics Working Papers ECO2008/16, European University Institute.
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    73. Stavros Degiannakis, 2023. "The D-model for GDP nowcasting," Working Papers 317, Bank of Greece.
    74. Schumacher, Christian, 2014. "MIDAS regressions with time-varying parameters: An application to corporate bond spreads and GDP in the Euro area," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100289, Verein für Socialpolitik / German Economic Association.
    75. Thomas Flavin & Ekaterini Panopoulou & Theologos Pantelidis, 2009. "Forecasting growth and inflation in an enlarged euro area," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(5), pages 405-425.
    76. In Choi, 2012. "Model Selection for Factor Analysis: Some New Criteria and Performance Comparisons," Working Papers 1209, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    77. Christian Schumacher, 2007. "Forecasting German GDP using alternative factor models based on large datasets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(4), pages 271-302.
    78. Modugno, Michele, 2011. "Nowcasting inflation using high frequency data," Working Paper Series 1324, European Central Bank.
    79. Chris Brooks & Adrian Fernandez-Perez & Joëlle Miffre & Ogonna Nneji, 2014. "Commodity Risk Factors and the Cross-Section of Equity Returns," ICMA Centre Discussion Papers in Finance icma-dp2014-09, Henley Business School, University of Reading.
    80. In Choi, 2011. "Efficient Estimation of Nonstationary Factor Models," Working Papers 1101, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy), revised Jun 2011.
    81. António Rua, 2016. "A wavelet-based multivariate multiscale approach for forecasting," Working Papers w201612, Banco de Portugal, Economics and Research Department.
    82. de Bondt, Gabe J. & Hahn, Elke & Zekaite, Zivile, 2021. "ALICE: Composite leading indicators for euro area inflation cycles," International Journal of Forecasting, Elsevier, vol. 37(2), pages 687-707.
    83. Hilde C. Bjørnland & Leif Brubakk & Anne Sofie Jore, 2006. "Forecasting inflation with an uncertain output gap," Working Paper 2006/02, Norges Bank.
    84. Dimitra Lamprou, 2015. "Nowcasting GDP in Greece: A Note on Forecasting Improvements from the Use of Bridge Models," South-Eastern Europe Journal of Economics, Association of Economic Universities of South and Eastern Europe and the Black Sea Region, vol. 13(1), pages 85-100.
    85. Sandra Eickmeier & Christina Ziegler, 2008. "How successful are dynamic factor models at forecasting output and inflation? A meta-analytic approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(3), pages 237-265.
    86. Donadelli, Michael & Paradiso, Antonio & Riedel, Max, 2016. "A quasi real-time leading indicator for the EU industrial production," SAFE Working Paper Series 118 [rev.], Leibniz Institute for Financial Research SAFE, revised 2016.
    87. Y. H. Venus Lun & John Carlton & Khaild Bichou, 2016. "Examining the economic impact of transport complex economies," Journal of Shipping and Trade, Springer, vol. 1(1), pages 1-17, December.
    88. Massimiliano Marcellino & Christian Schumacher, 2010. "Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 518-550, August.
    89. Panopoulou, Ekaterini, 2007. "Predictive financial models of the euro area: A new evaluation test," International Journal of Forecasting, Elsevier, vol. 23(4), pages 695-705.
    90. Greg Tkacz, 2007. "Gold Prices and Inflation," Staff Working Papers 07-35, Bank of Canada.

  128. Marcellino, Massimiliano & Corielli, Francesco, 2002. "Factor Based Index Tracking," CEPR Discussion Papers 3265, C.E.P.R. Discussion Papers.

    Cited by:

    1. Forni, Mario & Hallin, Marc & Lippi, Marco & Zaffaroni, Paolo, 2015. "Dynamic factor models with infinite-dimensional factor spaces: One-sided representations," Journal of Econometrics, Elsevier, vol. 185(2), pages 359-371.
    2. Cesarone, Francesco & Lampariello, Lorenzo & Sagratella, Simone, 2019. "A risk-gain dominance maximization approach to enhanced index tracking," Finance Research Letters, Elsevier, vol. 29(C), pages 231-238.
    3. Paskalis Glabadanidis, 2020. "Portfolio Strategies to Track and Outperform a Benchmark," JRFM, MDPI, vol. 13(8), pages 1-26, August.
    4. Daniel Giamouridis & Sandra Paterlini, 2010. "Regular(Ized) Hedge Fund Clones," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 33(3), pages 223-247, September.
    5. Yu Zheng & Bowei Chen & Timothy M. Hospedales & Yongxin Yang, 2019. "Index Tracking with Cardinality Constraints: A Stochastic Neural Networks Approach," Papers 1911.05052, arXiv.org, revised Nov 2019.
    6. Paskalis Glabadanidis & Leon Zolotoy, 2013. "Benchmark replication portfolio strategies," Journal of Asset Management, Palgrave Macmillan, vol. 14(2), pages 95-110, April.
    7. Derigs, Ulrich & Marzban, Shehab, 2009. "New strategies and a new paradigm for Shariah-compliant portfolio optimization," Journal of Banking & Finance, Elsevier, vol. 33(6), pages 1166-1176, June.
    8. Mario Forni & Marc Hallin & Marco Lippi & Paolo Zaffaroni, 2011. "One-Sided Representations of Generalized Dynamic Factor Models," EIEF Working Papers Series 1106, Einaudi Institute for Economics and Finance (EIEF), revised Mar 2011.
    9. Reza Bradrania & Davood Pirayesh Neghab & Mojtaba Shafizadeh, 2022. "State-dependent stock selection in index tracking: a machine learning approach," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 36(1), pages 1-28, March.
    10. Sant’Anna, Leonardo R. & Filomena, Tiago P. & Caldeira, João F., 2017. "Index tracking and enhanced indexing using cointegration and correlation with endogenous portfolio selection," The Quarterly Review of Economics and Finance, Elsevier, vol. 65(C), pages 146-157.
    11. Onatski, A. & Wang, C., 2020. "Spurious Factor Analysis," Cambridge Working Papers in Economics 2003, Faculty of Economics, University of Cambridge.
    12. Aboura, Sofiane & Chevallier, Julien, 2017. "A new weighting-scheme for equity indexes," International Review of Financial Analysis, Elsevier, vol. 54(C), pages 159-175.
    13. Liang-chuan Wu & I-chan Tsai, 2014. "Three fuzzy goal programming models for index portfolios," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(8), pages 1155-1169, August.
    14. Jiang, Pan & Perez, M. Fabricio, 2021. "Follow the leader: Index tracking with factor models," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 337-350.
    15. Blitz, David & Huij, Joop, 2012. "Evaluating the performance of global emerging markets equity exchange-traded funds," Emerging Markets Review, Elsevier, vol. 13(2), pages 149-158.
    16. Erdemlioglu, Deniz & Joliet, Robert, 2019. "Long-term asset allocation, risk tolerance and market sentiment," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 1-19.
    17. Forni, Mario & Cavicchioli, Maddalena & Lippi, Marco & Zaffaroni, Paolo, 2016. "Eigenvalue Ratio Estimators for the Number of Common Factors," CEPR Discussion Papers 11440, C.E.P.R. Discussion Papers.
    18. Xiangyu Cui & Xuan Zhang, 2021. "Index tracking strategy based on mixed-frequency financial data," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-15, April.
    19. Donatien Tafin Djoko & Yves Till�, 2015. "Selection of balanced portfolios to track the main properties of a large market," Quantitative Finance, Taylor & Francis Journals, vol. 15(2), pages 359-370, February.
    20. Casarin, Roberto & Grassi, Stefano & Ravazzolo, Francesco & van Dijk, Herman K., 2023. "A flexible predictive density combination for large financial data sets in regular and crisis periods," Journal of Econometrics, Elsevier, vol. 237(2).
    21. Chang, Ya-Ting & Gau, Yin-Feng & Hsu, Chih-Chiang, 2017. "Liquidity Commonality in Foreign Exchange Markets During the Global Financial Crisis and the Sovereign Debt Crisis: Effects of Macroeconomic and Quantitative Easing Announcements," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 172-192.
    22. Boldin, Michael & Cici, Gjergji, 2010. "The index fund rationality paradox," Journal of Banking & Finance, Elsevier, vol. 34(1), pages 33-43, January.
    23. Chuting Sun & Qi Wu & Xing Yan, 2023. "Dynamic CVaR Portfolio Construction with Attention-Powered Generative Factor Learning," Papers 2301.07318, arXiv.org, revised Jan 2024.
    24. Yu Zheng & Timothy M. Hospedales & Yongxin Yang, 2018. "Diversity and Sparsity: A New Perspective on Index Tracking," Papers 1809.01989, arXiv.org, revised Feb 2020.
    25. Wu, Dexiang & Dash Wu, Desheng, 2019. "An enhanced decision support approach for learning and tracking derivative index," Omega, Elsevier, vol. 88(C), pages 63-76.
    26. Canakgoz, N.A. & Beasley, J.E., 2009. "Mixed-integer programming approaches for index tracking and enhanced indexation," European Journal of Operational Research, Elsevier, vol. 196(1), pages 384-399, July.

  129. Marcellino, Massimiliano, 2002. "Forecast Pooling for Short Time Series of Macroeconomic Variables," CEPR Discussion Papers 3313, C.E.P.R. Discussion Papers.

    Cited by:

    1. Hilde C. Bjørnland & Karsten Gerdrup & Anne Sofie Jore & Christie Smith & Leif Anders Thorsrud, 2012. "Does Forecast Combination Improve Norges Bank Inflation Forecasts?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 74(2), pages 163-179, April.
    2. Marcellino, Massimiliano, 2002. "Forecasting EMU Macroeconomic Variables," CEPR Discussion Papers 3529, C.E.P.R. Discussion Papers.
    3. Carriero, Andrea & Marcellino, Massimiliano, 2007. "A comparison of methods for the construction of composite coincident and leading indexes for the UK," International Journal of Forecasting, Elsevier, vol. 23(2), pages 219-236.
    4. Luiz Renato Regis de Oliveira Lima & Wagner Piazza Gaglianone, 2012. "Constructing Optimal Density Forecasts from Point Forecast Combinations," Série Textos para Discussão (Working Papers) 5, Programa de Pós-Graduação em Economia - PPGE, Universidade Federal da Paraíba.
    5. Roberto S. Mariano & Suleyman Ozmucur, 2021. "Predictive Performance of Mixed-Frequency Nowcasting and Forecasting Models (with Application to Philippine Inflation and GDP Growth)," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 383-400, December.
    6. Marco Aiolfi & Carlo Ambrogio Favero, "undated". "Model Uncertainty, Thick Modelling and the predictability of Stock Returns," Working Papers 221, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    7. Costas Milas & Philip Rothman, 2007. "Out-of-Sample Forecasting of Unemployment Rates with Pooled STVECM Forecasts," Working Paper series 49_07, Rimini Centre for Economic Analysis.
    8. Mariola Pilatowska, 2009. "The Combined Forecasts Using the Akaike Weights," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 9, pages 5-16.
    9. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "Pooling versus model selection for nowcasting with many predictors: an application to German GDP," Discussion Paper Series 1: Economic Studies 2009,03, Deutsche Bundesbank.
    10. Theodosiou, Marina, 2011. "Forecasting monthly and quarterly time series using STL decomposition," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1178-1195, October.
    11. Carlo Favero & Massimiliano Marcellino, 2005. "Modelling and Forecasting Fiscal Variables for the Euro Area," Working Papers 298, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    12. Timmermann, Allan, 2006. "Forecast Combinations," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 4, pages 135-196, Elsevier.
    13. Saeed Rasekhi, 2011. "Fundamental Modeling Exchange Rate using Genetic Algorithm: A Case Study of European Countries," Journal of Economics and Behavioral Studies, AMH International, vol. 3(6), pages 352-359.
    14. Antonio Musa, 2022. "Nowcasting Bosnia and Herzegovina GDP in Real Time," IHEID Working Papers 08-2022, Economics Section, The Graduate Institute of International Studies.
    15. Costas Milas & Phil Rothman, 2005. "Multivariate STAR Unemployment Rate Forecasts," Econometrics 0502010, University Library of Munich, Germany.
    16. Elliott, Graham & Timmermann, Allan, 2004. "Optimal forecast combinations under general loss functions and forecast error distributions," Journal of Econometrics, Elsevier, vol. 122(1), pages 47-79, September.
    17. Laura Carabotta & Peter Claeys, 2015. "Combine to compete: improving fiscal forecast accuracy over time," UB School of Economics Working Papers 2015/320, University of Barcelona School of Economics.
    18. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2005. "Leading Indicators for Euro‐area Inflation and GDP Growth," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 785-813, December.
    19. Nikodinoska, Dragana & Käso, Mathias & Müsgens, Felix, 2022. "Solar and wind power generation forecasts using elastic net in time-varying forecast combinations," Applied Energy, Elsevier, vol. 306(PA).
    20. Bhaghoe, Sailesh & Ooft, Gavin, 2021. "Nowcasting Quarterly GDP Growth in Suriname with Factor-MIDAS and Mixed-Frequency VAR Models," Studies in Applied Economics 176, The Johns Hopkins Institute for Applied Economics, Global Health, and the Study of Business Enterprise.
    21. David G. McMillan & Mark E. Wohar, 2010. "Stock return predictability and dividend-price ratio: a nonlinear approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 351-365.
    22. Benjamin Russo, 2010. "Is past prologue? Prospects for state and local sales tax bases," Applied Economics, Taylor & Francis Journals, vol. 42(18), pages 2261-2274.
    23. Bordignon, Silvano & Bunn, Derek W. & Lisi, Francesco & Nan, Fany, 2013. "Combining day-ahead forecasts for British electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 88-103.

  130. Favero, Carlo A. & Marcellino, Massimiliano, 2001. "Large Datasets, Small Models and Monetary Policy in Europe," CEPR Discussion Papers 3098, C.E.P.R. Discussion Papers.

    Cited by:

    1. Marcellino, Massimiliano, 2002. "Forecasting EMU Macroeconomic Variables," CEPR Discussion Papers 3529, C.E.P.R. Discussion Papers.
    2. Marcellino, Massimiliano, 2002. "Some Stylized Facts on Non-Systematic Fiscal Policy in the Euro Area," CEPR Discussion Papers 3635, C.E.P.R. Discussion Papers.
    3. Belviso Francesco & Milani Fabio, 2006. "Structural Factor-Augmented VARs (SFAVARs) and the Effects of Monetary Policy," The B.E. Journal of Macroeconomics, De Gruyter, vol. 6(3), pages 1-46, December.
    4. Julen Esteban‐Pretel & Elisa Faraglia, 2010. "Monetary Shocks in a Model with Skill Loss," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(7), pages 1235-1265, October.
    5. Guido Turnip, 2017. "Identification of Small Open Economy SVARs via Markov-Switching Heteroskedasticity," The Economic Record, The Economic Society of Australia, vol. 93(302), pages 465-483, September.
    6. Mark J. Holmes & Arthur Grimes, 2005. "Is there long-run convergence of regional house prices in the UK?," Working Papers 05_11, Motu Economic and Public Policy Research.
    7. Rebucci, Alessandro & Ciccarelli, Matteo, 2004. "Has the Transmission Mechanism of European Monetary Policy Changed in the Run-Up to EMU?," CEPR Discussion Papers 4535, C.E.P.R. Discussion Papers.
    8. Pappa, Evi & Molteni, Francesco, 2017. "The Combination of Monetary and Fiscal Policy Shocks: A TVP-FAVAR Approach," CEPR Discussion Papers 12541, C.E.P.R. Discussion Papers.
    9. Bjørnland, Hilde C., 2005. "Monetary policy and exchange rate interactions in a small open economy," Memorandum 31/2005, Oslo University, Department of Economics.
    10. Hilde C. Bjørnland, 2009. "Monetary policy and exchange rate overshooting: Dornbusch was right after all," Working Paper 2009/09, Norges Bank.
    11. Carlo Ambrogio Favero & Massimilano Marcellino & Francesca Neglia, "undated". "Principal components at work: The empirical analysis of monetary policy with large datasets," Working Papers 223, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    12. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    13. Fabio C. Bagliano & Claudio Morana, 2006. "A New Approach to Factor Vector Autoregressive Estimation with an Application to Large-Scale Macroeconometric Modelling," Carlo Alberto Notebooks 28, Collegio Carlo Alberto.
    14. Reichlin, Lucrezia & Sala, Luca & Giannone, Domenico, 2002. "Tracking Greenspan: Systematic and Unsystematic Monetary Policy Revisited," CEPR Discussion Papers 3550, C.E.P.R. Discussion Papers.
    15. Victor Bystrov, 2006. "Forecasting Emerging Market Indicators: Brazil and Russia," Economics Working Papers ECO2006/12, European University Institute.
    16. Lucrezia Reichlin, 2003. "Factor models in large cross sections of time series," ULB Institutional Repository 2013/10179, ULB -- Universite Libre de Bruxelles.
    17. Fabio Bagliano & Claudio Morana, 2008. "Factor vector autoregressive estimation: a new approach," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 3(1), pages 15-23, June.
    18. Hilde C. Bjørnland, 2006. "Monetary Policy and the Illusionary Exchange Rate Puzzle," Computing in Economics and Finance 2006 45, Society for Computational Economics.
    19. Mark J. Holmes, 2005. "Integration or Independence? An Alternative Assessment of Real Interest Rate Linkages in the European Union," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 34(3), pages 407-427, November.
    20. Hae-shin Hwang & Woong Kim, 2012. "Estimation of Hybrid Phillips Curve: A Source of Conflicting Empirical Results," Southern Economic Journal, John Wiley & Sons, vol. 78(4), pages 1265-1288, April.
    21. Bai, Jushan, 2004. "Estimating cross-section common stochastic trends in nonstationary panel data," Journal of Econometrics, Elsevier, vol. 122(1), pages 137-183, September.
    22. Francesco Daveri & Andrea Mascotto, "undated". "The IT revolution across the U.S. states," Working Papers 226, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    23. S. G. B Henry & A. R. Pagan, 2004. "The Econometrics of the New Keynesian Policy Model: Introduction," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(s1), pages 581-607, September.

  131. Banerjee, A. & Marcellino, M. & Osbat, C., 2000. "Some Cautions on the Use of Panel Methods for Integrated Series of Macro-economic Data," Economics Working Papers eco2000/20, European University Institute.

    Cited by:

    1. Christian Dreger & Hans-Eggert Reimers, 2006. "Hysteresis and Persistence in the Course of Unemployment: The EU and US Experience," Discussion Papers of DIW Berlin 572, DIW Berlin, German Institute for Economic Research.
    2. Thomas H. W. Ziesemer, 2023. "Semi-endogenous growth in a non-Walrasian DSEM for Brazil: estimation and simulation of changes in foreign income, human capital, R&D, and terms of trade," Economic Change and Restructuring, Springer, vol. 56(2), pages 1147-1183, April.
    3. Belke, Ansgar & Dreger, Christian, 2011. "Current Account Imbalances in the Euro Area: Catching up or Competitiveness?," Ruhr Economic Papers 241, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    4. Michel Cyrille Samba & Seabrook Arthur Mveng, 2023. "Substitution Between Private and Government Consumption in a Currency Area: The Case of the CFA Franc Zone," Public Finance Review, , vol. 51(3), pages 432-450, May.
    5. Ana G. Bus y José L. Nicolini-Llosa, 2015. "La renta diferencial agrícola en Argentina en 1986-2008, con datos de panel y co-integración," Económica, Departamento de Economía, Facultad de Ciencias Económicas, Universidad Nacional de La Plata, vol. 61, pages 53-79, January-D.
    6. Carl S. Bonham & Peter Fuleky & Qianxue Zhao, 2013. "Estimating Demand Elasticities in Non-Stationary Panels: The Case of Hawaii's Tourism Industry," Working Papers 201303, University of Hawaii at Manoa, Department of Economics.
    7. Francesca Iorio & Stefano Fachin, 2014. "Savings and investments in the OECD: a panel cointegration study with a new bootstrap test," Empirical Economics, Springer, vol. 46(4), pages 1271-1300, June.
    8. Dang, Vinh Q.T. & So, Erin P.K. & Yang, Alan Yu & Chan, Kenneth S., 2020. "China and international market integration: Evidence from the law of one price in the Middle East and Africa," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    9. Karaman Örsal, Deniz Dilan & Droge, Bernd, 2014. "Panel cointegration testing in the presence of a time trend," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 377-390.
    10. Emmanuel Owusu-Sekyere & Francis M. Kemegue & Reneé van Eyden, 2011. "Remittances and the Dutch disease in Sub-Saharan Africa. A Dynamic Panel Approach," Working Papers 259, Economic Research Southern Africa.
    11. Rehme, G¸nther, 2002. "Why Run a Million Regressions? Endogenous Policy and Cross-Country Growth Empirics," Royal Economic Society Annual Conference 2002 157, Royal Economic Society.
    12. Christophe Hurlin & Valérie Mignon, 2006. "Une synthèse des tests de cointégration sur données de panel," Working Papers halshs-00070887, HAL.
    13. Kaddour Hadri & Eiji Kurozumi & Yao Rao, 2015. "Novel panel cointegration tests emending for cross‐section dependence with N fixed," Econometrics Journal, Royal Economic Society, vol. 18(3), pages 363-411, October.
    14. Mariam Camarero & Inmaculada Martínez-Zarzoso & Felicitas Nowak-Lehmann & Cecilio Tamarit, 2016. "Trade Openness and Income: A Tale of Two Regions," The World Economy, Wiley Blackwell, vol. 39(3), pages 386-408, March.
    15. Everaert, Gerdie, 2014. "A panel analysis of the fisher effect with an unobserved I(1) world real interest rate," Economic Modelling, Elsevier, vol. 41(C), pages 198-210.
    16. Bhattacharya, Mita & Narayan, Paresh, 2015. "Output and labor productivity in organized manufacturing: A panel cointegration analysis for India," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 171-177.
    17. Ziesemer, Thomas, 2019. "Can we have growth when population is stagnant? Testing linear growth rate formulas and their cross-unit cointegration of non-scale endogenous growth models," MERIT Working Papers 2019-021, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    18. Betty Daniel & Christos Shiamptanis, 2012. "Pushing the Limit? Fiscal Policy in the European Monetary Union," Working Papers 033, Ryerson University, Department of Economics.
    19. Pedro M. G. Martins, 2010. "Aid Absorption and Spending in Africa: A Panel Cointegration Approach," Working Paper Series 1010, Department of Economics, University of Sussex Business School.
    20. Vishal Chandr Jaunky & Robert Lundmark, 2015. "Are Shocks to Wood Fuel Production Permanent? Evidence from the EU," Energies, MDPI, vol. 8(11), pages 1-11, November.
    21. Kula Ferit & Aslan Alper, 2010. "Hysteresis vs. Natural Rate of Unemployment: One, the Other, or Both?," South East European Journal of Economics and Business, Sciendo, vol. 5(1), pages 91-94, April.
    22. Imed Drine & Christophe Rault, 2008. "Purchasing Power Parity for Developing and Developed Countries. What can we Learn from Non-Stationary Panel Data Models?," CESifo Working Paper Series 2255, CESifo.
    23. Burret, Heiko T. & Feld, Lars P. & Köhler, Ekkehard A., 2015. "(Un-)Sustinability of public finances in German Laender: A panel time series approach," Freiburg Discussion Papers on Constitutional Economics 15/09, Walter Eucken Institut e.V..
    24. G.S. Chen & Y. Yao & Julien Malizard, 2017. "Does foreign direct investment crowd in or crowd out private domestic investment in China? The effect of entry mode," Post-Print hal-03124847, HAL.
    25. Jaap W. B. Bos & Bertrand Candelon & Claire Economidou, 2016. "Does knowledge spill over across borders and technology regimes?," Journal of Productivity Analysis, Springer, vol. 46(1), pages 63-82, August.
    26. Hubrich, Kirstin & Marcellino, Massimiliano & Beck, Günter W., 2006. "Regional inflation dynamics within and across euro area countries and a comparison with the US," Working Paper Series 681, European Central Bank.
    27. Syed A. Basher & Josep Lluis Carrión-i-Silvestre, 2008. "Deconstructing Shocks and Persistence in OECD Real Exchange Rates," Working Papers XREAP2008-06, Xarxa de Referència en Economia Aplicada (XREAP), revised Jun 2008.
    28. Tolga Omay & Mübariz Hasanov & Yongcheol Shin, 2018. "Testing for Unit Roots in Dynamic Panels with Smooth Breaks and Cross-Sectionally Dependent Errors," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 167-193, June.
    29. Günther Rehme, 2002. "(Re-)Distribution of Personal Incomes, Education and Economic Performance Across Countries," CESifo Working Paper Series 711, CESifo.
    30. João Sousa Andrade, 2006. "Mobilidade do Capital e Sustentabilidade Externa: uma aplicação da tese de F-H a Portugal (1910-2004)," GEMF Working Papers 2006-04, GEMF, Faculty of Economics, University of Coimbra.
    31. António AFONSO & Christophe RAULT, 2008. "What do we Really Know About Fiscal Sustainability in the EU? A Panel Data Diagnostic," EcoMod2008 23800000, EcoMod.
    32. Sarah M. Lein & Thomas Maag, 2011. "The Formation Of Inflation Perceptions: Some Empirical Facts For European Countries," Scottish Journal of Political Economy, Scottish Economic Society, vol. 58(2), pages 155-188, May.
    33. Mathilde Aubry & Jean Bonnet & Patricia Renou-Maissant, 2015. "Entrepreneurship and the business cycle: the “Schumpeter” effect versus the “refugee” effect—a French appraisal based on regional data," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 54(1), pages 23-55, January.
    34. Beckmann, Joscha & Belke, Ansgar & Czudaj, Robert, 2014. "Does global liquidity drive commodity prices?," Journal of Banking & Finance, Elsevier, vol. 48(C), pages 224-234.
    35. Dreger, Christian, 2010. "Does the nominal exchange rate regime affect the real interest parity condition?," The North American Journal of Economics and Finance, Elsevier, vol. 21(3), pages 274-285, December.
    36. Bhavesh Garg & Pravakar Sahoo, 2021. "DO DIFFERENT TYPES OF CAPITAL INFLOWS HAVE DIFFERENTIAL IMPACT ON OUTPUT? Evidence from Time series and Panel Analysis," IEG Working Papers 443, Institute of Economic Growth.
    37. Westerlund, Joakim & Basher, Syed A., 2008. "Mixed signals among tests for panel cointegration," Economic Modelling, Elsevier, vol. 25(1), pages 128-136, January.
    38. Berka, Martin, 2006. "Non-linear adjustment in law of one price deviations and physical characteristics of goods," MPRA Paper 8606, University Library of Munich, Germany, revised Dec 2007.
    39. Antonia Arsova, 2019. "Exchange rate pass-through to import prices in Europe: A panel cointegration approach," Working Paper Series in Economics 384, University of Lüneburg, Institute of Economics.
    40. Peter Pedroni & Tim Vogelsang, 2005. "Robust Unit Root and Cointegration Rank Tests for Panels and Large Systems," Department of Economics Working Papers 2005-04, Department of Economics, Williams College.
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    1. Leal, Teresa & Pérez, Javier J. & Tujula, Mika & Vidal, Jean-Pierre, 2007. "Fiscal forecasting: lessons from the literature and challenges," Working Paper Series 843, European Central Bank.
    2. Fabrizio Balassone & Maura Francese & Stefania Zotteri, 2010. "Cyclical asymmetry in fiscal variables in the EU," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 37(4), pages 381-402, November.
    3. Kinnunen, Helvi & Kuoppamäki, Pasi, 1998. "Sustainability of public finances in Finland and the four largest Euro-area Economies," Bank of Finland Research Discussion Papers 25/1998, Bank of Finland.
    4. Gordon L. Brady & Cosimo Magazzino, 2018. "Sustainability and comovement of government debt in EMU Countries: A panel data analysis," Southern Economic Journal, John Wiley & Sons, vol. 85(1), pages 189-202, July.
    5. Magazzino, Cosimo & Brady, Gordon L. & Forte, Francesco, 2019. "A panel data analysis of the fiscal sustainability of G-7 countries," The Journal of Economic Asymmetries, Elsevier, vol. 20(C).
    6. Alberto Bagnai, 2004. "Keynesian And Neoclassical Fiscal Sustainability Indicators, With Applications To Emu Member Countries," Public Economics 0411005, University Library of Munich, Germany.
    7. Wolfgang Kitterer, 2007. "Nachhaltige Finanz‐ und Investitionspolitik der Bundesländer," Perspektiven der Wirtschaftspolitik, Verein für Socialpolitik, vol. 8(S1), pages 53-83, April.
    8. Gerrit B. Koester & Christoph Priesmeier, 2013. "Does Wagner´s Law Ruin the Sustainability of German Public Finances?," FinanzArchiv: Public Finance Analysis, Mohr Siebeck, Tübingen, vol. 69(3), pages 256-288, September.
    9. Gordon L. Brady & Cosimo Magazzino, 2018. "Fiscal Sustainability in the EU," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 46(3), pages 297-311, September.
    10. Bravo, Ana Bela Santos & Silvestre, Antonio Luis, 2002. "Intertemporal sustainability of fiscal policies: some tests for European countries," European Journal of Political Economy, Elsevier, vol. 18(3), pages 517-528, September.
    11. Cosimo Magazzino & Marco Mele, 2022. "A Dynamic Factor and Neural Networks Analysis of the Co-movement of Public Revenues in the EMU," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 8(2), pages 289-338, July.
    12. António Afonso, 2005. "Fiscal Sustainability: The Unpleasant European Case," FinanzArchiv: Public Finance Analysis, Mohr Siebeck, Tübingen, vol. 61(1), pages 19-44, March.
    13. Fabrizio BALASSONE & Maura FRANCESE, 2010. "Cyclical Asymmetry in Fiscal Policy, Debt Accumulation and the Treaty of Maastricht," EcoMod2004 330600014, EcoMod.
    14. Bystrov, Victor & Mackiewicz, Michał, 2020. "Recurrent explosive public debts and the long-run fiscal sustainability," Journal of Policy Modeling, Elsevier, vol. 42(2), pages 437-450.
    15. Javier Biscarri & Fernando Gracia, 2004. "Stock market cycles and stock market development in Spain," Spanish Economic Review, Springer;Spanish Economic Association, vol. 6(2), pages 127-151, July.
    16. Artis, Michael & Buti, Marco, 2000. ""Close to Balance or in Surplus": A Policy Maker's Guide to the Implementation of the Stability and Growth Pact," CEPR Discussion Papers 2515, C.E.P.R. Discussion Papers.
    17. Cunado, J. & Gil-Alana, L. A. & Perez de Gracia, F., 2004. "Is the US fiscal deficit sustainable?: A fractionally integrated approach," Journal of Economics and Business, Elsevier, vol. 56(6), pages 501-526.
    18. Carabotta, Laura & Paluzie, Elisenda & Ramos, Raul, 2017. "Does fiscal responsibility matter? Evidence from public and private forecasters in Italy," International Journal of Forecasting, Elsevier, vol. 33(3), pages 694-706.
    19. Cosimo Magazzino & Francesco Forte & Lorenzo Giolli, 2022. "On the Italian public accounts' sustainability: A wavelet approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 943-952, January.
    20. Merola, Rossana & Pérez, Javier J., 2014. "Fiscal Forecast Errors: Governments Versus Independent Agencies?," Papers RB2014/1/1, Economic and Social Research Institute (ESRI).
    21. Fabrizio Balassone & Maura Francese & Stefania Zotteri, 2008. "Cyclical asymmetry in fiscal variables," Temi di discussione (Economic working papers) 671, Bank of Italy, Economic Research and International Relations Area.
    22. Huseyin Kalyoncu, 2005. "Fiscal policy sustainability: test of intertemporal borrowing constraints," Applied Economics Letters, Taylor & Francis Journals, vol. 12(15), pages 957-962.
    23. Peter Claeys, 2007. "Sustainability of EU fiscal policies, a panel test," IREA Working Papers 200702, University of Barcelona, Research Institute of Applied Economics, revised Jan 2007.
    24. Chinn, Menzie David & Frankel, Jeffrey A., 2003. "The Euro Area and World Interest Rates," Santa Cruz Department of Economics, Working Paper Series qt2nb2h4zr, Department of Economics, UC Santa Cruz.
    25. Laura Carabotta, 2014. "Which Agency and Which Period is The Best? Analyzing National and International Fiscal Forecasts in Italy," International Journal of Economic Sciences, Prague University of Economics and Business, vol. 2014(1), pages 27-46.
    26. Strauch, Rolf, 1999. "Monitoring fiscal adjustments in the European Union and EMU," Discussion Paper Series 1: Economic Studies 1999,04, Deutsche Bundesbank.
    27. Cosimo Magazzino & Mihai Mutascu, 2019. "A wavelet analysis of Italian fiscal sustainability," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 8(1), pages 1-13, December.
    28. António Afonso, 2000. "Fiscal policy sustainability: some unpleasant European evidence," Working Papers Department of Economics 2000/12, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    29. Heiko T. Burret & Lars P. Feld & Kö & Ekkehard A. hler, 2014. "Panel Cointegration Tests on the Fiscal Sustainability of German Laender," CESifo Working Paper Series 4929, CESifo.

  135. Marcellino, M., 1997. "Temporal Disaggregation, Missing Observations, Outliers, and Forecasting: A Unifying Non-Model Based Procedures," Economics Working Papers eco97/30, European University Institute.

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  136. Massimiliano Marcellino, "undated". "Further Results on MSFE Encompassing," Working Papers 143, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Kenneth S. Rogoff & Vania Stavrakeva, 2008. "The Continuing Puzzle of Short Horizon Exchange Rate Forecasting," NBER Working Papers 14071, National Bureau of Economic Research, Inc.

  137. Massimiliano Marcellino & Grayham E. Mizon, "undated". "Small system modelling of real wages, inflation, unemployment and output per capita in Italy 1970-1994," Working Papers 188, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Eilev S. Jansen, 2004. "Modelling inflation in the Euro Area," Working Paper 2004/10, Norges Bank.
    2. Melike Bildirici & Elçin Aykaç Alp, 2012. "Minimum wage is efficient wage in Turkish labor market: TAR–cointegration analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(4), pages 1261-1270, June.
    3. Rita Duarte, 2009. "The dynamic effects of shocks to wages and prices in the United States and the Euro Area," Working Papers w200915, Banco de Portugal, Economics and Research Department.
    4. Sauro Mocetti & Guglielmo Barone, 2013. "Natural disasters, economic growth and corruption: a tale from two earthquakes," ERSA conference papers ersa13p726, European Regional Science Association.
    5. Kurita, Takamitsu, 2010. "Empirical modeling of Japan's markup and inflation, 1976-2000," Journal of Asian Economics, Elsevier, vol. 21(6), pages 552-563, December.
    6. Guglielmo Barone & Sauro Mocetti, 2014. "Natural disasters, growth and institutions: a tale of two earthquakes," Temi di discussione (Economic working papers) 949, Bank of Italy, Economic Research and International Relations Area.
    7. Gil-Alana, Luis A., 2003. "A Univariate Analysis of Unemployment and Inflation in Italy: A Fractionally Integrated Approach," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 23(2), November.
    8. Jolejole-Foreman, Maria Christina & Mallory, Mindy L. & Baylis, Katherine R., 2013. "Impact of Wheat and Rice Export Ban on Indian Market Integration," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150595, Agricultural and Applied Economics Association.
    9. Paolo Paruolo & Riccardo Girardi, 2010. "Wages and prices in Europe before and after the onset of the Monetary Union," Economics and Quantitative Methods qf1009, Department of Economics, University of Insubria.
    10. David F. Hendry & Massimiliano Marcellino & Chiara Monfardini, 2008. "Foreword," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 711-714, December.
    11. Mariam Camarero & Gaetano D’Adamo & Cecilio Tamarit, 2018. "Differences in wage determination in the Eurozone," Working Papers 1811, Department of Applied Economics II, Universidad de Valencia.
    12. Binotti, Annetta Maria & Ghiani, Enrico, 2008. "Changes in aggregate supply conditions in Italy: A small econometric model and its policy implications," Journal of Policy Modeling, Elsevier, vol. 30(6), pages 1017-1039.
    13. Camarero, Mariam & D’Adamo, Gaetano & Tamarit, Cecilio, 2021. "Differences in wage determination in the Eurozone: A challenge to the resilience of the common currency," Journal of Policy Modeling, Elsevier, vol. 43(1), pages 183-199.

  138. Eliana La Ferrara & Massimiliano Marcellino, "undated". "TFP, Costs, and Public Infrastructure: An Equivocal Relationship," Working Papers 176, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Duarte Bom, P.R. & Ligthart, J.E., 2008. "How Productive is Public Capital? A Meta-Analysis," Other publications TiSEM e841076c-c1df-4617-a1bd-9, Tilburg University, School of Economics and Management.
    2. Balázs Égert & Tomasz Koźluk & Douglas Sutherland, 2009. "Infrastructure and Growth: Empirical Evidence," OECD Economics Department Working Papers 685, OECD Publishing.
    3. E. Marrocu & R. Paci, 2006. "The effects of public capital on the productivity of the Italian regions," Working Paper CRENoS 200613, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    4. Federico Bonaglia & Eliana La Ferrara & Massimiliano Marcellino, 2000. "Public Capital and Economic Performance: Evidence from Italy," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 59(2), pages 221-244, September.
    5. Leonzio Rizzo & Riccardo Secomandi, 2018. "Lo stock di capitale comunale: indicazioni per le politiche di intervento infrastrutturale di Regione Lombardia," Working Papers 2018117, University of Ferrara, Department of Economics.
    6. Romp, Ward & de Haan, Jakob, 2005. "Public capital and economic growth: a critical survey," EIB Papers 2/2005, European Investment Bank, Economics Department.
    7. Trofimov, Ivan D., 2020. "Public capital and productive economy profits: evidence from OECD economies," MPRA Paper 106848, University Library of Munich, Germany.
    8. Guido Ascari & Valeria Di Cosmo, 2005. "Determinants of Total Factor Productivity in the Italian Regions," Macroeconomics 0511009, University Library of Munich, Germany.
    9. Davide Piacentino, 2008. "Productivity, Infrastructures and Convergence: Panel Data Evidence on Italian Regions," SCIENZE REGIONALI, FrancoAngeli Editore, vol. 2008(2), pages 5-26.
    10. Torrisi, Gianpiero, 2009. "A multilevel analysis on the economic impact of public infrastructure and corruption on Italian regions," MPRA Paper 15487, University Library of Munich, Germany.
    11. Valter Di Giacinto & Giacinto Micucci & Pasqualino Montanaro, 2009. "Dynamic macroeconomic effects of public capital: evidence from regional Italian data," Temi di discussione (Economic working papers) 733, Bank of Italy, Economic Research and International Relations Area.
    12. Víctor Adame & Javier Alonso & Luisa Pérez & David Tuesta, 2017. "Infrastructure & economic growth from a meta-analysis approach: do all roads lead to Rome?," Working Papers 17/07, BBVA Bank, Economic Research Department.
    13. R. Pala & E. Marrocu & R. Paci, 2000. "Estimation of total factor productivity for regions and sectors in Italy. A panel cointegration approach," Working Paper CRENoS 200016, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    14. Pedro R.D. Bom & Jenny E. Ligthart, 2009. "How Productive is Public Capital? A Meta-Regression Analysis," International Center for Public Policy Working Paper Series, at AYSPS, GSU paper0912, International Center for Public Policy, Andrew Young School of Policy Studies, Georgia State University.
    15. Francesco Aiello & Camilla Mastromarco & Angelo Zago, 2011. "Be productive or face decline. On the sources and determinants of output growth in Italian manufacturing firms," Empirical Economics, Springer, vol. 41(3), pages 787-815, December.
    16. Salvatore Amico Roxas & Antonio Cristofaro & Giuseppe Piroli, 2012. "Public Capital in the Private Sector of Italian Economy," EERI Research Paper Series EERI_RP_2012_19, Economics and Econometrics Research Institute (EERI), Brussels.
    17. Farhadi, Minoo, 2015. "Transport infrastructure and long-run economic growth in OECD countries," Transportation Research Part A: Policy and Practice, Elsevier, vol. 74(C), pages 73-90.
    18. Torrisi, Gianpiero, 2009. "Infrastructures and economic performance: a critical comparison across four approaches," MPRA Paper 18688, University Library of Munich, Germany.
    19. Federici, Andrea, 2018. "Il rapporto tra capitale pubblico e altre variabili macroeconomiche: analisi della letteratura [The relationship between public capital and other macroeconomic variable: a literature review]," MPRA Paper 88515, University Library of Munich, Germany.
    20. Torrisi, Gianpiero, 2009. "Public infrastructure: definition, classification and measurement issues," MPRA Paper 12990, University Library of Munich, Germany.
    21. Daniel Montolio & Albert Solé‐Ollé, 2009. "Road investment and regional productivity growth: the effects of vehicle intensity and congestion," Papers in Regional Science, Wiley Blackwell, vol. 88(1), pages 99-118, March.
    22. Juan A. Núñez-Serrano & Francisco J. Velázquez, 2017. "Is Public Capital Productive? Evidence from a Meta-analysis," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 39(2), pages 313-345.

  139. Federico Bonaglia & Eliana La Ferrara & Massimiliano Marcellino, "undated". "Public Capital and Economic Performance: Evidence from Italy," Working Papers 163, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Raffaello Bronzini & Paolo Piselli, 2006. "Determinants of long-run regional productivity: the role of R&D, human capital and public infrastructure," Temi di discussione (Economic working papers) 597, Bank of Italy, Economic Research and International Relations Area.
    2. Duarte Bom, P.R. & Ligthart, J.E., 2008. "How Productive is Public Capital? A Meta-Analysis," Other publications TiSEM e841076c-c1df-4617-a1bd-9, Tilburg University, School of Economics and Management.
    3. Angel de la Fuente, 2010. "Infrastructures and Productivity: an Updated Survey," Working Papers 475, Barcelona School of Economics.
    4. E. Marrocu & R. Paci, 2006. "The effects of public capital on the productivity of the Italian regions," Working Paper CRENoS 200613, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    5. Bo Zhou & Yanping Xu & Seul Ki Lee, 2019. "Tourism development and regional production efficiency: Evidence from southwestern China," Tourism Economics, , vol. 25(5), pages 800-818, August.
    6. Francesco Aiello & Alfonsina Iona & Leone Leonida, 2012. "Regional infrastructure and firm investment: theory and empirical evidence for Italy," Empirical Economics, Springer, vol. 42(3), pages 835-862, June.
    7. Paolo Pinotti, 2012. "The economic costs of organized crime: evidence from southern Italy," Temi di discussione (Economic working papers) 868, Bank of Italy, Economic Research and International Relations Area.
    8. Eliana La Ferrara & Massimiliano Marcellino, "undated". "TFP, Costs, and Public Infrastructure: An Equivocal Relationship," Working Papers 176, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    9. Gwenaelle Poilon & Jérôme Creel, 2008. "Is public capital productive in Europe?," SciencePo Working papers Main hal-03416671, HAL.
    10. Jinrui Zhang & Ruilian Zhang & Junzhuo Xu & Jie Wang & Guoqing Shi, 2021. "Infrastructure Investment and Regional Economic Growth: Evidence from Yangtze River Economic Zone," Land, MDPI, vol. 10(3), pages 1-14, March.
    11. Valter Di Giacinto & Giorgio Nuzzo, 2006. "Explaining labour productivity differentials across Italian regions: the role of socio‐economic structure and factor endowments," Papers in Regional Science, Wiley Blackwell, vol. 85(2), pages 299-320, June.
    12. Romp, Ward & de Haan, Jakob, 2005. "Public capital and economic growth: a critical survey," EIB Papers 2/2005, European Investment Bank, Economics Department.
    13. Joanna Mackiewicz-Łyziak, 2010. "Wpływ infrastruktury na produktywność w gospodarce Polski," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 3, pages 41-61.
    14. Guido Ascari & Valeria Di Cosmo, 2005. "Determinants of Total Factor Productivity in the Italian Regions," Macroeconomics 0511009, University Library of Munich, Germany.
    15. Torrisi, Gianpiero, 2009. "A multilevel analysis on the economic impact of public infrastructure and corruption on Italian regions," MPRA Paper 15487, University Library of Munich, Germany.
    16. Francesco Aiello & Valeria Pupo, 2009. "Structural Funds And Economic Divide In Italy," Working Papers 200914, Università della Calabria, Dipartimento di Economia, Statistica e Finanza "Giovanni Anania" - DESF.
    17. Roberto Iacono, 2014. "No blessing, no curse? On the benefits of being a resource-rich southern region of Italy," Working Paper Series 15914, Department of Economics, Norwegian University of Science and Technology.
    18. Valter Di Giacinto & Giacinto Micucci & Pasqualino Montanaro, 2009. "Dynamic macroeconomic effects of public capital: evidence from regional Italian data," Temi di discussione (Economic working papers) 733, Bank of Italy, Economic Research and International Relations Area.
    19. Xinhai Lu & Jiao Hou & Yifeng Tang & Ting Wang & Tianyi Li & Xupeng Zhang, 2022. "Evaluating the Impact of the Highway Infrastructure Construction and the Threshold Effect on Cultivated Land Use Efficiency: Evidence from Chinese Provincial Panel Data," Land, MDPI, vol. 11(7), pages 1-20, July.
    20. Víctor Adame & Javier Alonso & Luisa Pérez & David Tuesta, 2017. "Infrastructure & economic growth from a meta-analysis approach: do all roads lead to Rome?," Working Papers 17/07, BBVA Bank, Economic Research Department.
    21. R. Pala & E. Marrocu & R. Paci, 2000. "Estimation of total factor productivity for regions and sectors in Italy. A panel cointegration approach," Working Paper CRENoS 200016, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    22. Fedderke, Johannes W. & Bogetic & Zeljko, 2006. "Infrastructure and growth in South Africa : direct and indirect productivity impacts of 19 infrastructure measures," Policy Research Working Paper Series 3989, The World Bank.
    23. Beckman, Jayson & Hertel, Thomas, 2009. "Why Previous Estimates of the Cost of Climate Mitigation Might Be Too Low," Conference papers 331860, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    24. Silvia Bertarelli, 2006. "Public capital and growth," Politica economica, Società editrice il Mulino, issue 3, pages 361-398.
    25. Raffaello Bronzini & Paolo Piselli, 2005. "What determines productivity level in the long run? Evidence from Italians regions," ERSA conference papers ersa05p267, European Regional Science Association.
    26. Anu Tokila & Mika Haapanen, 2012. "Evaluation of Deadweight Spending in Regional Enterprise Financing," Regional Studies, Taylor & Francis Journals, vol. 46(2), pages 185-201, May.
    27. Valter Di Giacinto & Giacinto Micucci & Pasqualino Montanaro, 2012. "The Macroeconomic Impact of Infrastructures: A Literature Review and Empirical Analysis on the Case of Italy," QA - Rivista dell'Associazione Rossi-Doria, Associazione Rossi Doria, issue 1, March.
    28. Pedro R.D. Bom & Jenny E. Ligthart, 2009. "How Productive is Public Capital? A Meta-Regression Analysis," International Center for Public Policy Working Paper Series, at AYSPS, GSU paper0912, International Center for Public Policy, Andrew Young School of Policy Studies, Georgia State University.
    29. Francesco Aiello & Camilla Mastromarco & Angelo Zago, 2011. "Be productive or face decline. On the sources and determinants of output growth in Italian manufacturing firms," Empirical Economics, Springer, vol. 41(3), pages 787-815, December.
    30. Kemmerling, Achim & Stephan, Andreas, 2015. "Comparative political economy of regional transport infrastructure investment in Europe," Journal of Comparative Economics, Elsevier, vol. 43(1), pages 227-239.
    31. Xueliang Zhang, 2008. "Transport infrastructure, spatial spillover and economic growth: Evidence from China," Psychometrika, Springer;The Psychometric Society, vol. 3(4), pages 585-597, December.
    32. Barilla, David & Carlucci, Fabio & Cirà, Andrea & Ioppolo, Giuseppe & Siviero, Lucio, 2020. "Total factor logistics productivity: A spatial approach to the Italian regions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 205-222.
    33. Salvatore Amico Roxas & Antonio Cristofaro & Giuseppe Piroli, 2012. "Public Capital in the Private Sector of Italian Economy," EERI Research Paper Series EERI_RP_2012_19, Economics and Econometrics Research Institute (EERI), Brussels.
    34. Jordan Roulleau-Pasdeloup, 2013. "The Productive Government Spending Multiplier, In and Out of The Zero Lower Bound," Working Papers 2013-02, Center for Research in Economics and Statistics.
    35. Melo, Patricia C. & Graham, Daniel J. & Brage-Ardao, Ruben, 2013. "The productivity of transport infrastructure investment: A meta-analysis of empirical evidence," Regional Science and Urban Economics, Elsevier, vol. 43(5), pages 695-706.
    36. E. Marrocu & R. Paci & F. Pigliaru, 2006. "Gli effetti del capitale pubblico sulla produttività delle regioni italiane," Working Paper CRENoS 200601, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    37. Torrisi, Gianpiero, 2009. "Infrastructures and economic performance: a critical comparison across four approaches," MPRA Paper 18688, University Library of Munich, Germany.
    38. Zhang, Liyunpeng & Zhuang, Yuhang & Ding, Yibing & Liu, Ziwei, 2023. "Infrastructure and poverty reduction: Assessing the dynamic impact of Chinese infrastructure investment in sub-Saharan Africa," Journal of Asian Economics, Elsevier, vol. 84(C).
    39. Li, Yan & Chen, Zhenhua & Wang, Peng, 2020. "Impact of high-speed rail on urban economic efficiency in China," Transport Policy, Elsevier, vol. 97(C), pages 220-231.
    40. Bronzini, Raffaello & Piselli, Paolo, 2009. "Determinants of long-run regional productivity with geographical spillovers: The role of R&D, human capital and public infrastructure," Regional Science and Urban Economics, Elsevier, vol. 39(2), pages 187-199, March.
    41. Papagni, Erasmo & Lepore, Amedeo & Felice, Emanuele & Baraldi, Anna Laura & Alfano, Maria Rosaria, 2021. "Public investment and growth: Lessons learned from 60-years experience in Southern Italy," Journal of Policy Modeling, Elsevier, vol. 43(2), pages 376-393.
    42. Mao, Xia & Chen, Xiao, 2023. "Does airport construction narrow regional economic disparities in China?," Journal of Air Transport Management, Elsevier, vol. 108(C).
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    46. Erasmo Papagni & Amedeo Lepore & Emanuele Felice & Anna Laura Baraldi & Maria Rosaria Alfano, 2018. "Public Investment and Growth Accelerations: The Case of Southern Italy, 1951-1995," EERI Research Paper Series EERI RP 2018/10, Economics and Econometrics Research Institute (EERI), Brussels.
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  140. Carlo Ambrogio Favero & Massimilano Marcellino & Francesca Neglia, "undated". "Principal components at work: The empirical analysis of monetary policy with large datasets," Working Papers 223, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Fabio C. Bagliano & Claudio Morana, 2006. "International Macroeconomic Dynamics: a Factor Vector Autoregressive Approach," ICER Working Papers 41-2006, ICER - International Centre for Economic Research.
    2. Claudio Morana, 2010. "Heteroskedastic Factor Vector Autoregressive Estimation of Persistent and Non Persistent Processes Subject to Structural Breaks," ICER Working Papers - Applied Mathematics Series 36-2010, ICER - International Centre for Economic Research.
    3. Farmer, Roger & Henry, Jerome & Marcellino, Massimiliano & Beyer, Andreas, 2005. "Factor Analysis in a New-Keynesian Model," CEPR Discussion Papers 5266, C.E.P.R. Discussion Papers.
    4. Forni, Mario & Hallin, Marc & Lippi, Marco & Zaffaroni, Paolo, 2015. "Dynamic factor models with infinite-dimensional factor spaces: One-sided representations," Journal of Econometrics, Elsevier, vol. 185(2), pages 359-371.
    5. Marcellino, Massimiliano & Sivec, Vasja, 2016. "Monetary, fiscal and oil shocks: Evidence based on mixed frequency structural FAVARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 335-348.
    6. Carlos Cesar Trucios-Maza & João H. G Mazzeu & Luis K. Hotta & Pedro L. Valls Pereira & Marc Hallin, 2019. "On the robustness of the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Working Papers ECARES 2019-32, ULB -- Universite Libre de Bruxelles.
    7. Dimitris Korobilis, 2008. "Forecasting in vector autoregressions with many predictors," Advances in Econometrics, in: Bayesian Econometrics, pages 403-431, Emerald Group Publishing Limited.
    8. Norkutė, Milda & Sarafidis, Vasilis & Yamagata, Takashi & Cui, Guowei, 2021. "Instrumental variable estimation of dynamic linear panel data models with defactored regressors and a multifactor error structure," Journal of Econometrics, Elsevier, vol. 220(2), pages 416-446.
    9. Barigozzi, Matteo & Conti, Antonio & Luciani, Matteo, 2012. "Do Euro area countries respond asymmetrically to the common monetary policy?," LSE Research Online Documents on Economics 43344, London School of Economics and Political Science, LSE Library.
    10. Erdemlioglu, Deniz, 2009. "Macro Factors in UK Excess Bond Returns: Principal Components and Factor-Model Approach," MPRA Paper 28895, University Library of Munich, Germany.
    11. Alonso Gomez & John M Maheu & Alex Maynard, 2008. "Improving Forecasts of Inflation using the Term Structure of Interest Rates," Working Papers tecipa-319, University of Toronto, Department of Economics.
    12. Fabio C. Bagliano & Claudio Morana, 2007. "Business Cycle Comovement in the G-7: Common Shocks or Common Transmission Mechanisms?," Carlo Alberto Notebooks 40, Collegio Carlo Alberto.
    13. Hubrich, Kirstin & Marcellino, Massimiliano & Beck, Günter W., 2006. "Regional inflation dynamics within and across euro area countries and a comparison with the US," Working Paper Series 681, European Central Bank.
    14. Jalali-Naini , Ahmad. R. & Hemati , Maryam, 2012. "The Effect of Monetary Shocks on Disaggregated Prices in a Data Rich Environment: a Bayesian FAVAR Approach," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 6(4), pages 27-60, July.
    15. Sarafidis, Vasilis & Yamagata, Takashi, 2010. "Instrumental Variable Estimation of Dynamic Linear Panel Data Models with Defactored Regressors under Cross-sectional Dependence," MPRA Paper 25182, University Library of Munich, Germany.
    16. Qin, Duo, 2007. "Uncover Latent PPP by Dynamic Factor Error Correction Model (DF-ECM) Approach: Evidence from five OECD countries," Economics Discussion Papers 2007-29, Kiel Institute for the World Economy (IfW Kiel).
    17. Ergemen, Yunus Emre & Rodríguez-Caballero, C. Vladimir, 2023. "Estimation of a dynamic multi-level factor model with possible long-range dependence," International Journal of Forecasting, Elsevier, vol. 39(1), pages 405-430.
    18. Hansen, Stephen & McMahon, Michael, 2015. "Shocking language: Understanding the macroeconomic effects of central bank communication," Economic Research Papers 269727, University of Warwick - Department of Economics.
    19. Marta Bańbura, 2008. "Large Bayesian VARs," 2008 Meeting Papers 334, Society for Economic Dynamics.
    20. Banerjee, Anindya & Marcellino, Massimiliano, 2008. "Factor-augmented Error Correction Models," CEPR Discussion Papers 6707, C.E.P.R. Discussion Papers.
    21. Galariotis, Emilios & Makrichoriti, Panagiota & Spyrou, Spyros, 2018. "The impact of conventional and unconventional monetary policy on expectations and sentiment," Journal of Banking & Finance, Elsevier, vol. 86(C), pages 1-20.
    22. Dias Francisco & Rua António & Pinheiro Maximiano, 2013. "Determining the number of global and country-specific factors in the euro area," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(5), pages 573-617, December.
    23. He, Qing & Leung, Pak-Ho & Chong, Terence Tai-Leung, 2013. "Factor-augmented VAR analysis of the monetary policy in China," China Economic Review, Elsevier, vol. 25(C), pages 88-104.
    24. Mönch, Emanuel, 2005. "Forecasting the yield curve in a data-rich environment: a no-arbitrage factor-augmented VAR approach," Working Paper Series 544, European Central Bank.
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    26. Qin, Duo & Cagas, Marie Anne & Ducanes, Geoffrey & Magtibay-Ramos, Nedelyn & Quising, Pilipinas F., 2007. "Measuring Regional Market Integration in Developing Asia: a Dynamic Factor Error Correction Model (DF-ECM) Approach," Working Papers on Regional Economic Integration 8, Asian Development Bank.
    27. Ilse Botha, 2010. "A Comparative Analysis Of The Synchronisation Of Business Cycles For Developed And Developing Economies With The World Business Cycle," South African Journal of Economics, Economic Society of South Africa, vol. 78(2), pages 192-207, June.
    28. Gianluca Lagana, 2004. "Measuring monetary policy in the UK: a factor augmented vector autoregressive approach," Money Macro and Finance (MMF) Research Group Conference 2004 64, Money Macro and Finance Research Group.
    29. Omer Bayar, 2022. "Reducing large datasets to improve the identification of estimated policy rules," Empirical Economics, Springer, vol. 63(1), pages 113-140, July.
    30. Pappa, Evi & Molteni, Francesco, 2017. "The Combination of Monetary and Fiscal Policy Shocks: A TVP-FAVAR Approach," CEPR Discussion Papers 12541, C.E.P.R. Discussion Papers.
    31. Jörg Breitung & Sandra Eickmeier, 2006. "Dynamic factor models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 27-42, March.
    32. Trucíos Maza, Carlos César & Mazzeu, João H. G. & Hotta, Luiz Koodi & Pereira, Pedro L. Valls & Hallin, Marc, 2020. "Robustness and the general dynamic factor model with infinite-dimensional space: identification, estimation, and forecasting," Textos para discussão 521, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    33. Beck, Guenter W. & Hubrich, Kirstin & Marcellino, Massimiliano, 2009. "On the importance of sectoral shocks for price-setting," CFS Working Paper Series 2009/32, Center for Financial Studies (CFS).
    34. Blaes, Barno, 2009. "Money and monetary policy transmission in the euro area: evidence from FAVAR- and VAR approaches," Discussion Paper Series 1: Economic Studies 2009,18, Deutsche Bundesbank.
    35. Guowei Cui & Vasilis Sarafidis & Takashi Yamagata, 2020. "IV Estimation of Spatial Dynamic Panels with Interactive Effects: Large Sample Theory and an Application on Bank Attitude," Monash Econometrics and Business Statistics Working Papers 11/20, Monash University, Department of Econometrics and Business Statistics.
    36. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    37. Massimiliano Serati & Matteo Manera & Michele Plotegher, 2008. "Modelling electricity prices: from the state of the art to a draft of a new proposal," LIUC Papers in Economics 210, Cattaneo University (LIUC).
    38. Stock, James H. & Watson, Mark, 2011. "Dynamic Factor Models," Scholarly Articles 28469541, Harvard University Department of Economics.
    39. Lu, Biao & Wu, Liuren, 2009. "Macroeconomic releases and the interest rate term structure," Journal of Monetary Economics, Elsevier, vol. 56(6), pages 872-884, September.
    40. Lasse Bork, 2009. "Estimating US Monetary Policy Shocks Using a Factor-Augmented Vector Autoregression: An EM Algorithm Approach," CREATES Research Papers 2009-11, Department of Economics and Business Economics, Aarhus University.
    41. Helmut Lütkepohl, 2014. "Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey," SFB 649 Discussion Papers SFB649DP2014-004, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    42. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
    43. Andreas Beyer & Roger E. A. Farmer & Jérôme Henry & Massimiliano Marcellino, 2007. "Factor Analysis in a Model with Rational Expectations," NBER Working Papers 13404, National Bureau of Economic Research, Inc.
    44. Mario Forni & Marc Hallin & Marco Lippi & Paolo Zaffaroni, 2011. "One-Sided Representations of Generalized Dynamic Factor Models," EIEF Working Papers Series 1106, Einaudi Institute for Economics and Finance (EIEF), revised Mar 2011.
    45. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," PSE Working Papers halshs-02262202, HAL.
    46. Pallara, Kevin, 2016. "The dynamic effects of government spending: a FAVAR approach," MPRA Paper 92283, University Library of Munich, Germany.
    47. Giuliano Queiroz Ferreira & Leonardo Bornacki Mattos, 2022. "Regime-dependent price puzzle in the Brazilian economy: evidence from VAR and FAVAR approaches," SN Business & Economics, Springer, vol. 2(9), pages 1-28, September.
    48. Oyenyinka Sunday Omoshoro‐Jones & Lumengo Bonga‐Bonga, 2022. "Intra‐regional spillovers from Nigeria and South Africa to the rest of Africa: New evidence from a FAVAR model," The World Economy, Wiley Blackwell, vol. 45(1), pages 251-275, January.
    49. Krokida, Styliani-Iris & Makrychoriti, Panagiota & Spyrou, Spyros, 2020. "Monetary policy and herd behavior: International evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 170(C), pages 386-417.
    50. Zlatina Balabanova & Ralf Brüggemann, 2012. "External Information and Monetary Policy Transmission in New EU Member States: Results from FAVAR Models," Working Paper Series of the Department of Economics, University of Konstanz 2012-05, Department of Economics, University of Konstanz.
    51. Ergemen, Yunus Emre, 2023. "Parametric estimation of long memory in factor models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1483-1499.
    52. Hwee Kwan Chow & Keen Meng Choy, 2009. "Monetary Policy And Asset Prices In A Small Open Economy: A Factor-Augmented Var Analysis For Singapore," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 1-23.
    53. Kapetanios, George & Marcellino, Massimiliano, 2010. "Factor-GMM estimation with large sets of possibly weak instruments," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2655-2675, November.
    54. Eijffinger, S.C.W. & Mahieu, R.J. & Raes, L.B.D., 2010. "The Bond Yield Conundrum : Alternative Hypotheses and the State of the Economy," Other publications TiSEM 8b320ebf-1447-46c9-82e3-c, Tilburg University, School of Economics and Management.
    55. Fabio C. Bagliano & Claudio Morana, 2006. "A New Approach to Factor Vector Autoregressive Estimation with an Application to Large-Scale Macroeconometric Modelling," Carlo Alberto Notebooks 28, Collegio Carlo Alberto.
    56. Bodnar, Taras & Reiß, Markus, 2016. "Exact and asymptotic tests on a factor model in low and large dimensions with applications," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 125-151.
    57. Andrea Beltratti & Claudio Morana, 2008. "International shocks and national house prices," ICER Working Papers - Applied Mathematics Series 14-2008, ICER - International Centre for Economic Research.
    58. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2013. "Model Selection in Equations with Many ‘Small’ Effects," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(1), pages 6-22, February.
    59. Marc Hallin & Roman Liska, 2008. "Dynamic Factors in the Presence of Block Structure," Economics Working Papers ECO2008/22, European University Institute.
    60. Kapetanios, George & Marcellino, Massimiliano, 2010. "Cross-sectional averaging and instrumental variable estimation with many weak instruments," Economics Letters, Elsevier, vol. 108(1), pages 36-39, July.
    61. Barhoumi, K. & Darné, O. & Ferrara, L., 2013. "Dynamic Factor Models: A review of the Literature ," Working papers 430, Banque de France.
    62. George Kapetanios & Massimiliano Marcellino, 2009. "A parametric estimation method for dynamic factor models of large dimensions," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(2), pages 208-238, March.
    63. Guowei Cui & Vasilis Sarafidis & Takashi Yamagata, 2023. "IV estimation of spatial dynamic panels with interactive effects: large sample theory and an application on bank attitude towards risk," The Econometrics Journal, Royal Economic Society, vol. 26(2), pages 124-146.
    64. Mouloud El Hafidi & Marouane Daoui, 2019. "Chocs de la politique monétaire et croissance économique au Maroc : une approche en terme de modèles FAVAR," Post-Print hal-03311354, HAL.
    65. Berner, Anne & Bruns, Stephan B. & Moneta, Alessio & Stern, David I., 2021. "Do energy efficiency improvements reduce energy use? Empirical evidence on the economy-wide rebound effect in Europe and the United States," University of Göttingen Working Papers in Economics 422, University of Goettingen, Department of Economics.
    66. Bayar, Omer, 2018. "Weak instruments and estimated monetary policy rules," Journal of Macroeconomics, Elsevier, vol. 58(C), pages 308-317.
    67. Jean Boivin & Marc P. Giannoni & Benoît Mojon, 2008. "How Has the Euro Changed the Monetary Transmission?," NBER Working Papers 14190, National Bureau of Economic Research, Inc.
    68. Massimiliano Marcellino & George Kapetanios, 2006. "The Role of Search Frictions and Bargaining for Inflation Dynamics," Working Papers 305, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    69. Fabio Bagliano & Claudio Morana, 2008. "Factor vector autoregressive estimation: a new approach," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 3(1), pages 15-23, June.
    70. In Choi & Dukpa Kim & Yun Jung Kim & Noh-Sun Kwark, 2016. "A Multilevel Factor Model: Identification, Asymptotic Theory and Applications," Working Papers 1609, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    71. Maldonado, Javier & Ruiz Ortega, Esther, 2017. "Accurate Subsampling Intervals of Principal Components Factors," DES - Working Papers. Statistics and Econometrics. WS 23974, Universidad Carlos III de Madrid. Departamento de Estadística.
    72. Harun Mirza & Lidia Storjohann, 2014. "Making Weak Instrument Sets Stronger: Factor‐Based Estimation of Inflation Dynamics and a Monetary Policy Rule," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(4), pages 643-664, June.
    73. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers halshs-02262202, HAL.
    74. Stavros Degiannakis, 2023. "The D-model for GDP nowcasting," Working Papers 317, Bank of Greece.
    75. António Rua & Francisco Craveiro Dias, 2008. "Determining the number of factors in approximate factor models with global and group-specific factors," Working Papers w200809, Banco de Portugal, Economics and Research Department.
    76. Sánchez-Fung, José R., 2011. "Estimating monetary policy reaction functions for emerging market economies: The case of Brazil," Economic Modelling, Elsevier, vol. 28(4), pages 1730-1738, July.
    77. Choi, In, 2012. "Efficient Estimation Of Factor Models," Econometric Theory, Cambridge University Press, vol. 28(2), pages 274-308, April.
    78. In Choi, 2012. "Model Selection for Factor Analysis: Some New Criteria and Performance Comparisons," Working Papers 1209, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    79. Mirza, Harun & Storjohann, Lidia, 2011. "Making a Weak Instrument Set Stronger: Factor-Based Estimation of the Taylor Rule," Bonn Econ Discussion Papers 13/2011, University of Bonn, Bonn Graduate School of Economics (BGSE).
    80. Porshakov, Alexey & Deryugina, Elena & Ponomarenko, Alexey & Sinyakov, Andrey, 2015. "Nowcasting and short-term forecasting of Russian GDP with a dynamic factor model," BOFIT Discussion Papers 19/2015, Bank of Finland Institute for Emerging Economies (BOFIT).
    81. Wang, Zongrun & Zhou, Ling & Mi, Yunlong & Shi, Yong, 2022. "Measuring dynamic pandemic-related policy effects: A time-varying parameter multi-level dynamic factor model approach," Journal of Economic Dynamics and Control, Elsevier, vol. 139(C).
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    87. Duo Qin & Marie Anne Cagas & Geoffrey Ducanes & Nedelyn Magtibay-Ramos & Pilipinas F. Quising, 2006. "Measuring Regional Market Integration by Dynamic Factor Error Correction Model (DF-ECM) Approach - The Case of Developing Asia," Working Papers 565, Queen Mary University of London, School of Economics and Finance.
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  141. Giampiero M. Gallo & Massimiliano Marcellino, "undated". "Ex Post and Ex Ante Analysis of Provisional Data," Working Papers 141, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Andres Fernandez & Norman R. Swanson, 2009. "Real-time datasets really do make a difference: definitional change, data release, and forecasting," Working Papers 09-28, Federal Reserve Bank of Philadelphia.
    2. Fabio Busetti, 2006. "Preliminary data and econometric forecasting: an application with the Bank of Italy Quarterly Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(1), pages 1-23.
    3. Jacobs, Jan P.A.M. & van Norden, Simon, 2011. "Modeling data revisions: Measurement error and dynamics of "true" values," Journal of Econometrics, Elsevier, vol. 161(2), pages 101-109, April.
    4. Fabio Busetti, 2001. "The use of preliminary data in econometric forecasting: an application with the Bank of Italy Quarterly Model," Temi di discussione (Economic working papers) 437, Bank of Italy, Economic Research and International Relations Area.

  142. Massimiliano Marcellino & James H. Stock & Mark W. Watson, "undated". "Macroeconomic Forecasting in the Euro Area: Country Specific versus Area-Wide Information," Working Papers 201, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Frédérick Demers & Annie De Champlain, 2005. "Forecasting Core Inflation in Canada: Should We Forecast the Aggregate or the Components?," Staff Working Papers 05-44, Bank of Canada.
    2. Michael Graff, 2005. "Internationale Konjunkturverbunde," KOF Working papers 05-108, KOF Swiss Economic Institute, ETH Zurich.
    3. Stylianos Asimakopoulos & Joan Paredes & Thomas Warmedinger, 2020. "Real‐Time Fiscal Forecasting Using Mixed‐Frequency Data," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(1), pages 369-390, January.
    4. Robert Lehmann, 2016. "Economic Growth and Business Cycle Forecasting at the Regional Level," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65.
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  143. Hans-Martin Krolzig & Massimiliano Marcellino & Grayham E. Mizon, "undated". "A Markov-Switching Vector Equilibrium Correction Model of the UK Labour Market," Working Papers 185, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

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    18. Massimiliano Marcellino & Grayham E. Mizon, "undated". "Small system modelling of real wages, inflation, unemployment and output per capita in Italy 1970-1994," Working Papers 188, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    19. Haldrup; Niels & Morten Oerregaard Nielsen, 2005. "Directional Congestion and Regime Switching in a Long Memory Model for Electricity Prices," Economics Working Papers 2005-18, Department of Economics and Business Economics, Aarhus University.
    20. Mihai Mutascu & Scott Hegerty, 2023. "Predicting the contribution of artificial intelligence to unemployment rates: an artificial neural network approach," Post-Print hal-04273887, HAL.
    21. Theis Lange, 2009. "First and second order non-linear cointegration models," CREATES Research Papers 2009-04, Department of Economics and Business Economics, Aarhus University.
    22. Kausik Chaudhuri & Alok Kumar, 2015. "A Markov-Switching Model for Indian Stock Price and Volume," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 14(3), pages 239-257, December.
    23. Jorge Andrés Tamayo Castaño, 2012. "Asimetrías en la demanda por trabajo en Colombia: el papel del ciclo económico," Borradores de Economia 689, Banco de la Republica de Colombia.
    24. Isaac Abunyuwah & Henry De-Graft Acquah, 2013. "Modelling non-linear Spatial Market Integration and Equilibrium Processes in Hidden Markov Framework," Journal of Economics and Behavioral Studies, AMH International, vol. 5(8), pages 535-545.
    25. Dennis Kristensen & Anders Rahbek, 2007. "Likelihood-Based Inference in Nonlinear Error-Correction Models," CREATES Research Papers 2007-38, Department of Economics and Business Economics, Aarhus University.
    26. Sophocles N. Brissimis & George Hondroyiannis & Christos Papazoglou & Nicholas T. Tsaveas & Melina A. Vasardani, 2010. "Current account determinants and external sustainability in periods of structural change," Working Papers 117, Bank of Greece.
    27. Matteo Manera & Alessandro Cologni, 2006. "The Asymmetric Effects of Oil Shocks on Output Growth: A Markov-Switching Analysis for the G-7 Countries," Working Papers 2006.29, Fondazione Eni Enrico Mattei.
    28. de Morais, Igor Alexandre C. & Portugal, Marcelo Savino, 2005. "A Markov Switching Model for the Brazilian Demand for Imports: Analyzing the Import Substitution Process in Brazil," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 25(2), November.
    29. Vollmer, Teresa & von Cramon-Taubadel, Stephan, 2019. "The influence of Brazilian exports on price transmission processes in the coffee sector: A Markov-switching approach," DARE Discussion Papers 1904, Georg-August University of Göttingen, Department of Agricultural Economics and Rural Development (DARE).
    30. Carlo Di Giorgio, 2016. "Business Cycle Synchronization of CEECs with the Euro Area: A Regime Switching Approach," Journal of Common Market Studies, Wiley Blackwell, vol. 54(2), pages 284-300, March.
    31. Tillmann, Peter, 2003. "Cointegration and Regime-Switching Risk Premia in the U.S. Term Structure of Interest Rates," Bonn Econ Discussion Papers 27/2003, University of Bonn, Bonn Graduate School of Economics (BGSE).
    32. Hondroyiannis, George & Papapetrou, Evangelia, 2006. "Stock returns and inflation in Greece: A Markov switching approach," Review of Financial Economics, Elsevier, vol. 15(1), pages 76-94.
    33. Götz, Linde & Glauben, Thomas & Brümmer, Bernhard, 2013. "Wheat export restrictions and domestic market effects in Russia and Ukraine during the food crisis," Food Policy, Elsevier, vol. 38(C), pages 214-226.
    34. Tamayo Castaño, Jorge Andrés, 2012. "Asimetrías en la demanda por trabajo en Colombia : el papel del ciclo económico," Chapters, in: Arango-Thomas, Luis Eduardo & Hamann-Salcedo, Franz Alonso (ed.), El mercado de trabajo en Colombia : hechos, tendencias e instituciones, chapter 12, pages 487-542, Banco de la Republica de Colombia.
    35. Ricardo Troncoso-Sepúlveda, 2019. "Price transmission of rice in Colombia and the world," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 91, pages 151-179, Julio - D.
    36. Li, Leon, 2022. "The dynamic interrelations of oil-equity implied volatility indexes under low and high volatility-of-volatility risk," Energy Economics, Elsevier, vol. 105(C).
    37. Costas Milas & Phil Rothman, 2005. "Multivariate STAR Unemployment Rate Forecasts," Econometrics 0502010, University Library of Munich, Germany.
    38. Brümmer, Bernhard & Zorya, Sergiy, 2005. "Wheat / Flour Price Transmission and Agricultural Policies in Ukraine: A Markov-Switching Vector Error Correction Approach," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24624, European Association of Agricultural Economists.
    39. Ihle, Rico & von Cramon-Taubadel, Stephan, 2008. "Nonlinear Vector Error Correction Models in Price Transmission Analysis: Threshold Models vs. Markov-Switching Models," 2008 International Congress, August 26-29, 2008, Ghent, Belgium 44198, European Association of Agricultural Economists.
    40. Igor Alexandre Clemente de Morais & Marcelo Savino Portugal, 2003. "Business Cycle in the Industrial Production of Brazilian States," Anais do XXXI Encontro Nacional de Economia [Proceedings of the 31st Brazilian Economics Meeting] e75, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    41. Arash Refah-Kahriz & Hassan Heidari & Mahdiyeh Rahimdel, 2023. "Is there a similar Granger causality among CO2 emissions, energy consumption and economic growth in different regimes in Iran?," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(4), pages 3801-3822, April.
    42. Garcés Díaz Daniel, 2017. "Explaining Inflation with a Classical Dichotomy Model and Switching Monetary Regimes: Mexico 1932-2013," Working Papers 2017-20, Banco de México.
    43. Cerra, Valerie & Saxena, Sweta Chaman, 2010. "The monetary model strikes back: Evidence from the world," Journal of International Economics, Elsevier, vol. 81(2), pages 184-196, July.
    44. Fischer, Henning & Stolper, Oscar, 2019. "The nonlinear dynamics of corporate bond spreads: Regime-dependent effects of their determinants," Discussion Papers 08/2019, Deutsche Bundesbank.
    45. Mittnik, Stefan & Semmler, Willi, 2012. "Regime dependence of the fiscal multiplier," Journal of Economic Behavior & Organization, Elsevier, vol. 83(3), pages 502-522.
    46. Sandip Chakraborty & Ram Kumar Kakani & Bernadette C. Canasa, 2017. "Impact of International Outsourcing on Domestic Wage of Singapore Manufacturing Sector," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(6), pages 82-97, June.
    47. Kristensen, Dennis & Rahbek, Anders, 2010. "Likelihood-based inference for cointegration with nonlinear error-correction," Journal of Econometrics, Elsevier, vol. 158(1), pages 78-94, September.
    48. Emmanuel Hache & Frédéric Lantz, 2011. "Oil price volatility: An Econometric Analysis of the WTI Market," Working Papers hal-02472326, HAL.
    49. Balcilar, Mehmet & Hammoudeh, Shawkat & Asaba, Nwin-Anefo Fru, 2015. "A regime-dependent assessment of the information transmission dynamics between oil prices, precious metal prices and exchange rates," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 72-89.
    50. Chevallier, Julien, 2011. "Evaluating the carbon-macroeconomy relationship: Evidence from threshold vector error-correction and Markov-switching VAR models," Economic Modelling, Elsevier, vol. 28(6), pages 2634-2656.
    51. Ming-Yuan Leon Li & Chun-Nan Chen, 2010. "Examining the interrelation dynamics between option and stock markets using the Markov-switching vector error correction model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(7), pages 1173-1191.
    52. José Cancelo, 2007. "Cyclical Asymmetries in Unemployment Rates: International Evidence," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 13(3), pages 334-346, August.
    53. Pan, Xiongfeng & Uddin, Md. Kamal & Saima, Umme & Guo, Shucen & Guo, Ranran, 2019. "Regime switching effect of financial development on energy intensity: Evidence from Markov-switching vector error correction model," Energy Policy, Elsevier, vol. 135(C).
    54. David F. Hendry & Massimiliano Marcellino & Chiara Monfardini, 2008. "Foreword," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 711-714, December.
    55. PeterTillmann, 2004. "Cointegration and Regime-Switching Risk Premia in the U.S. Term Structure of Interest Rates," Computing in Economics and Finance 2004 53, Society for Computational Economics.
    56. Angelos Kanas, 2008. "Modeling regime transition in stock index futures markets and forecasting implications," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(8), pages 649-669.
    57. Maximo Camacho, 2002. "Nonlinear stochastic trends and economic fluctuations," Computing in Economics and Finance 2002 274, Society for Computational Economics.
    58. Declerck , Francis & Indjehagopian , Jean-Pierre & Bellocq , Flavien, 2015. "Relation entre le prix du pétrole et les cours boursiers des grandes compagnies pétrolières mondiales," ESSEC Working Papers WP1504, ESSEC Research Center, ESSEC Business School.
    59. Philip Kostov & John Lingard, 2004. "Regime-switching Vector Error Correction Model (VECM) analysis of UK meat consumption," Econometrics 0409007, University Library of Munich, Germany.
    60. Camacho, Maximo, 2005. "Markov-switching stochastic trends and economic fluctuations," Journal of Economic Dynamics and Control, Elsevier, vol. 29(1-2), pages 135-158, January.
    61. Evangelia Papapetrou, 2013. "Oil prices and economic activity in Greece," Economic Change and Restructuring, Springer, vol. 46(4), pages 385-397, November.
    62. Brümmer, Bernhard & von Cramon-Taubadel, Stephan & Zorya, Sergiy, 2006. "Vertical Price Transmission between Wheat and Flour in Ukraine: A Markov-Switching Vector Error Correction Approach," 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia 25575, International Association of Agricultural Economists.
    63. Jamel JOUINI, 2018. "Measuring the Macroeconomic Impacts of Fiscal Policy Shocks in the Saudi Economy : A Markov Switching Approach," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 55-70, December.
    64. Ihle, Rico & von Cramon-Taubadel, Stephan, 2008. "A Comparison of Threshold Cointegration and Markov-Switching Vector Error Correction Models in Price Transmission Analysis," 2008 Conference, April 21-22, 2008, St. Louis, Missouri 37603, NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    65. Sylvia Kaufmann, 2016. "Hidden Markov models in time series, with applications in economics," Working Papers 16.06, Swiss National Bank, Study Center Gerzensee.
    66. Rezitis, A.N. & Ahammad, S.M., 2015. "Investigating Agricultural Production Relations across Bangladesh, India and Pakistan Using Vector Error Correction and Markov-Switching Models," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 28(1).
    67. Manera, Matteo & Cologni, Alessandro, 2006. "The Asymmetric Effects of Oil Shocks on Output Growth: A Markov-Switching Analysis," International Energy Markets Working Papers 12121, Fondazione Eni Enrico Mattei (FEEM).

  144. Massimiliano Marcellino & Grayham E. Mizon, "undated". "Modelling shifts in the wage-price and unemployment-inflation relationships in Italy, Poland, and the UK," Working Papers 145, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Adriatik Hoxha, 2016. "The Wage-Price Setting Behavior: Comparing The Evidence from EU28 and EMU," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 19(60), pages 61-102, June.
    2. Rita Duarte, 2009. "The dynamic effects of shocks to wages and prices in the United States and the Euro Area," Working Papers w200915, Banco de Portugal, Economics and Research Department.
    3. Massimiliano Marcellino & Grayham E. Mizon & Hans-Martin Krolzig, 2002. "A Markov-switching vector equilibrium correction model of the UK labour market," Empirical Economics, Springer, vol. 27(2), pages 233-254.
    4. Ian Babetskii, 2006. "Aggregate Wage Flexibility in Selected New EU Member States," Working Papers 2006/1, Czech National Bank.
    5. Ana María Iregui & Jesús Otero, 2002. "On The Dynamics Of Unemployment In A Developing Economy: Colombia," Borradores de Economia 3298, Banco de la Republica.
    6. Jolejole-Foreman, Maria Christina & Mallory, Mindy L. & Baylis, Katherine R., 2013. "Impact of Wheat and Rice Export Ban on Indian Market Integration," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150595, Agricultural and Applied Economics Association.
    7. Silvia Fedeli & Francesco Forte, 2009. "The Laffer effects of a program of deregulation cum detaxation: the Italian reform of labour contracts in the period 1997–2001," European Journal of Law and Economics, Springer, vol. 27(3), pages 211-232, June.
    8. David F. Hendry & Massimiliano Marcellino & Chiara Monfardini, 2008. "Foreword," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 711-714, December.
    9. Binotti, Annetta Maria & Ghiani, Enrico, 2008. "Changes in aggregate supply conditions in Italy: A small econometric model and its policy implications," Journal of Policy Modeling, Elsevier, vol. 30(6), pages 1017-1039.
    10. Adriatik Hoxha, 2016. "The Switch to Near-Rational Wage-Price Setting Behaviour: The Case of United Kingdom," EuroEconomica, Danubius University of Galati, issue 1(35), pages 127-148, may.

  145. Massimiliano Marcellino, "undated". "Model Selection for Non-Linear Dynamic Models," Working Papers 159, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.

    Cited by:

    1. Kenneth Kasa, 2007. "Learning and Model Validation," 2007 Meeting Papers 548, Society for Economic Dynamics.

Articles

  1. Carriero, Andrea & Marcellino, Massimiliano & Tornese, Tommaso, 2023. "Macro uncertainty in the long run," Economics Letters, Elsevier, vol. 225(C).
    See citations under working paper version above.
  2. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023. "Tail Forecasting With Multivariate Bayesian Additive Regression Trees," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 979-1022, August.
    See citations under working paper version above.
  3. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Nowcasting tail risk to economic activity at a weekly frequency," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 843-866, August.
    See citations under working paper version above.
  4. Foroni, Claudia & Marcellino, Massimiliano & Stevanovic, Dalibor, 2022. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," International Journal of Forecasting, Elsevier, vol. 38(2), pages 596-612.
    See citations under working paper version above.
  5. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2022. "The global component of inflation volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 700-721, June.
    See citations under working paper version above.
  6. Yu Bai & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2022. "Macroeconomic forecasting in a multi‐country context," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1230-1255, September.
    See citations under working paper version above.
  7. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2021. "No‐arbitrage priors, drifting volatilities, and the term structure of interest rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 495-516, August.
    See citations under working paper version above.
  8. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano, 2021. "Using time-varying volatility for identification in Vector Autoregressions: An application to endogenous uncertainty," Journal of Econometrics, Elsevier, vol. 225(1), pages 47-73.
    See citations under working paper version above.
  9. Marcellino, Massimiliano & Sivec, Vasja, 2021. "Nowcasting Gdp Growth In A Small Open Economy," National Institute Economic Review, National Institute of Economic and Social Research, vol. 256, pages 127-161, April.

    Cited by:

    1. Christian Glocker & Serguei Kaniovski, 2022. "Macroeconometric forecasting using a cluster of dynamic factor models," Empirical Economics, Springer, vol. 63(1), pages 43-91, July.
    2. Rudrani Bhattacharya & Bornali Bhandari & Sudipto Mundle, 2023. "Nowcasting India’s Quarterly GDP Growth: A Factor-Augmented Time-Varying Coefficient Regression Model (FA-TVCRM)," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(1), pages 213-234, March.
    3. Hwee Kwan Chow & Yijie Fei & Daniel Han, 2023. "Forecasting GDP with many predictors in a small open economy: forecast or information pooling?," Empirical Economics, Springer, vol. 65(2), pages 805-829, August.

  10. Giraitis, Liudas & Kapetanios, George & Marcellino, Massimiliano, 2021. "Time-varying instrumental variable estimation," Journal of Econometrics, Elsevier, vol. 224(2), pages 394-415.
    See citations under working paper version above.
  11. Goulet Coulombe, Philippe & Marcellino, Massimiliano & Stevanović, Dalibor, 2021. "Can Machine Learning Catch The Covid-19 Recession?," National Institute Economic Review, National Institute of Economic and Social Research, vol. 256, pages 71-109, April.
    See citations under working paper version above.
  12. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020. "Assessing international commonality in macroeconomic uncertainty and its effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(3), pages 273-293, April.
    See citations under working paper version above.
  13. Pierre Guérin & Danilo Leiva-Leon & Massimiliano Marcellino, 2020. "Markov-Switching Three-Pass Regression Filter," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 285-302, April.
    See citations under working paper version above.
  14. Y. Dendramis & G. Kapetanios & M. Marcellino, 2020. "A similarity‐based approach for macroeconomic forecasting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 801-827, June.
    See citations under working paper version above.
  15. Christian Hepenstrick & Massimiliano Marcellino, 2019. "Forecasting gross domestic product growth with large unbalanced data sets: the mixed frequency three‐pass regression filter," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(1), pages 69-99, January.

    Cited by:

    1. Hwee Kwan Chow & Yijie Fei & Daniel Han, 2023. "Forecasting GDP with many predictors in a small open economy: forecast or information pooling?," Empirical Economics, Springer, vol. 65(2), pages 805-829, August.
    2. Chatelais, Nicolas & Stalla-Bourdillon, Arthur & Chinn, Menzie D., 2023. "Forecasting real activity using cross-sectoral stock market information," Journal of International Money and Finance, Elsevier, vol. 131(C).
    3. Nicolas Chatelais & Arthur Stalla-Bourdillon & Menzie D. Chinn, 2022. "Macroeconomic Forecasting using Filtered Signals from a Stock Market Cross Section," NBER Working Papers 30305, National Bureau of Economic Research, Inc.
    4. Mahmut Gunay, 2020. "Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial," Working Papers 2002, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.

  16. George Kapetanios & Massimiliano Marcellino & Fabrizio Venditti, 2019. "Large time‐varying parameter VARs: A nonparametric approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1027-1049, November.
    See citations under working paper version above.
  17. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano, 2019. "Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors," Journal of Econometrics, Elsevier, vol. 212(1), pages 137-154.

    Cited by:

    1. Ha, Jongrim & Kose, M. Ayhan & Ohnsorge, Franziska, 2021. "Inflation During the Pandemic: What Happened? What is Next?," MPRA Paper 108677, University Library of Munich, Germany.
    2. Bańbura, Marta & Leiva-Leon, Danilo & Menz, Jan-Oliver, 2021. "Do inflation expectations improve model-based inflation forecasts?," Working Paper Series 2604, European Central Bank.
    3. Leonardo Nogueira Ferreira & Silvia Miranda-Agrippino & Giovanni Ricco, 2023. "Bayesian Local Projections," Working Papers Series 581, Central Bank of Brazil, Research Department.
    4. Joshua C. C. Chan & Xuewen Yu, 2022. "Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility," Papers 2206.08438, arXiv.org.
    5. Gefang, Deborah & Koop, Gary & Poon, Aubrey, 2023. "Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage," International Journal of Forecasting, Elsevier, vol. 39(1), pages 346-363.
    6. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2023. "Large Time‐Varying Volatility Models for Hourly Electricity Prices," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 545-573, June.
    7. Annalisa Cadonna & Sylvia Fruhwirth-Schnatter & Peter Knaus, 2019. "Triple the gamma -- A unifying shrinkage prior for variance and variable selection in sparse state space and TVP models," Papers 1912.03100, arXiv.org.
    8. Christiane Baumeister & Dimitris Korobilis & Thomas K. Lee, 2022. "Energy Markets and Global Economic Conditions," The Review of Economics and Statistics, MIT Press, vol. 104(4), pages 828-844, October.
    9. Tsionas, Mike G. & Izzeldin, Marwan & Trapani, Lorenzo, 2022. "Estimation of large dimensional time varying VARs using copulas," European Economic Review, Elsevier, vol. 141(C).
    10. Martin Feldkircher & Florian Huber & Gary Koop & Michael Pfarrhofer, 2022. "APPROXIMATE BAYESIAN INFERENCE AND FORECASTING IN HUGE‐DIMENSIONAL MULTICOUNTRY VARs," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(4), pages 1625-1658, November.
    11. Niko Hauzenberger & Florian Huber & Luca Onorante, 2021. "Combining shrinkage and sparsity in conjugate vector autoregressive models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(3), pages 304-327, April.
    12. Gao, Shen & Hou, Chenghan & Nguyen, Bao H., 2021. "Forecasting natural gas prices using highly flexible time-varying parameter models," Economic Modelling, Elsevier, vol. 105(C).
    13. Joshua C.C. Chan & Eric Eisenstat & Chenghan Hou & Gary Koop, 2018. "Composite likelihood methods for large Bayesian VARs with stochastic volatility," CAMA Working Papers 2018-26, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    14. Dimitris Korobilis & Kenichi Shimizu, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Papers 2021_19, Business School - Economics, University of Glasgow.
    15. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2022. "Reconciled Estimates of Monthly GDP in the US," Working Papers 22-01, Federal Reserve Bank of Cleveland.
    16. Joshua C. C. Chan, 2022. "Comparing Stochastic Volatility Specifications for Large Bayesian VARs," Papers 2208.13255, arXiv.org.
    17. Niko Hauzenberger, 2020. "Flexible Mixture Priors for Large Time-varying Parameter Models," Papers 2006.10088, arXiv.org, revised Nov 2020.
    18. Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
    19. Javier Sánchez García & Salvador Cruz Rambaud, 2022. "Machine Learning Regularization Methods in High-Dimensional Monetary and Financial VARs," Mathematics, MDPI, vol. 10(6), pages 1-15, March.
    20. Florian, Huber & Koop, Gary & Onorante, Luca & Pfarrhofer, Michael & Schreiner, Josef, 2021. "Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs," Working Papers 2021-01, Joint Research Centre, European Commission.
    21. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2022. "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," CEPR Discussion Papers 17512, C.E.P.R. Discussion Papers.
    22. Chenghan Hou & Bao Nguyen & Bo Zhang, 2023. "Real‐time forecasting of the Australian macroeconomy using flexible Bayesian VARs," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 418-451, March.
    23. Kose, M. Ayhan & Ha, Jongrim & Ohnsorge, Franziska, 2022. "Global Stagflation," CEPR Discussion Papers 17381, C.E.P.R. Discussion Papers.
    24. Ramis Khabibullin & Sergei Seleznev, 2022. "Fast Estimation of Bayesian State Space Models Using Amortized Simulation-Based Inference," Papers 2210.07154, arXiv.org.
    25. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2020. "Large Time-Varying Volatility Models for Electricity Prices," Working Papers No 05/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    26. Florian Huber & Gary Koop, 2023. "Fast and Order-invariant Inference in Bayesian VARs with Non-Parametric Shocks," Papers 2305.16827, arXiv.org.
    27. Marcellino, Massimiliano & Bai, Yu & Carriero, Andrea & Clark, Todd, 2022. "Macroeconomic Forecasting in a Multi-country Context," CEPR Discussion Papers 16994, C.E.P.R. Discussion Papers.
    28. Loaiza-Maya, Rubén & Smith, Michael Stanley & Nott, David J. & Danaher, Peter J., 2022. "Fast and accurate variational inference for models with many latent variables," Journal of Econometrics, Elsevier, vol. 230(2), pages 339-362.
    29. Annalisa Cadonna & Sylvia Frühwirth-Schnatter & Peter Knaus, 2020. "Triple the Gamma—A Unifying Shrinkage Prior for Variance and Variable Selection in Sparse State Space and TVP Models," Econometrics, MDPI, vol. 8(2), pages 1-36, May.
    30. Arias, Jonas E. & Rubio-Ramírez, Juan F. & Shin, Minchul, 2023. "Macroeconomic forecasting and variable ordering in multivariate stochastic volatility models," Journal of Econometrics, Elsevier, vol. 235(2), pages 1054-1086.
    31. Hauzenberger, Niko & Pfarrhofer, Michael & Stelzer, Anna, 2021. "On the effectiveness of the European Central Bank’s conventional and unconventional policies under uncertainty," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 822-845.
    32. Ping Wu & Gary Koop, 2022. "Fast, Order-Invariant Bayesian Inference in VARs using the Eigendecomposition of the Error Covariance Matrix," Working Papers 2310, University of Strathclyde Business School, Department of Economics.
    33. Niko Hauzenberger & Florian Huber & Gary Koop, 2020. "Dynamic Shrinkage Priors for Large Time-varying Parameter Regressions using Scalable Markov Chain Monte Carlo Methods," Papers 2005.03906, arXiv.org, revised May 2023.
    34. Knotek, Edward S. & Zaman, Saeed, 2023. "Real-time density nowcasts of US inflation: A model combination approach," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1736-1760.
    35. Zhang, Wen, 2022. "China’s government spending and global inflation dynamics: The role of the oil price channel," Energy Economics, Elsevier, vol. 110(C).
    36. Marcellino, Massimiliano & Clark, Todd & Huber, Florian & Koop, Gary & Pfarrhofer, Michael, 2022. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," CEPR Discussion Papers 17461, C.E.P.R. Discussion Papers.
    37. Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023. "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Papers 2311.16333, arXiv.org, revised Apr 2024.
    38. Gregor Kastner & Florian Huber, 2020. "Sparse Bayesian vector autoregressions in huge dimensions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(7), pages 1142-1165, November.
    39. Dimitris Korobilis, 2020. "Sign restrictions in high-dimensional vector autoregressions," Working Paper series 20-09, Rimini Centre for Economic Analysis.
    40. Nadja Klein & Michael Stanley Smith & David J. Nott, 2023. "Deep distributional time series models and the probabilistic forecasting of intraday electricity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 493-511, June.
    41. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano & Mertens, Elmar, 2023. "Shadow-rate VARs," Discussion Papers 14/2023, Deutsche Bundesbank.
    42. Botelho, Vasco & Foroni, Claudia & Renzetti, Andrea, 2023. "Labour at risk," Working Paper Series 2840, European Central Bank.
    43. Niko Hauzenberger & Michael Pfarrhofer & Luca Rossini, 2020. "Sparse time-varying parameter VECMs with an application to modeling electricity prices," Papers 2011.04577, arXiv.org, revised Apr 2023.
    44. Hauzenberger, Niko, 2021. "Flexible Mixture Priors for Large Time-varying Parameter Models," Econometrics and Statistics, Elsevier, vol. 20(C), pages 87-108.
    45. Dimitris Korobilis, 2022. "A new algorithm for structural restrictions in Bayesian vector autoregressions," Papers 2206.06892, arXiv.org.
    46. Fischer, Manfred M. & Hauzenberger, Niko & Huber, Florian & Pfarrhofer, Michael, 2022. "General Bayesian time-varying parameter VARs for modeling government bond yields," Working Papers in Regional Science 2021/01, WU Vienna University of Economics and Business.
    47. Joshua C. C. Chan, 2019. "Minnesota-type adaptive hierarchical priors for large Bayesian VARs," CAMA Working Papers 2019-61, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    48. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino & Elmar Mertens, 2021. "Addressing COVID-19 Outliers in BVARs with Stochastic Volatility," Working Papers 21-02R, Federal Reserve Bank of Cleveland, revised 09 Aug 2021.
    49. Li, Chenxing & Maheu, John M & Yang, Qiao, 2022. "An Infinite Hidden Markov Model with Stochastic Volatility," MPRA Paper 115456, University Library of Munich, Germany.
    50. Ankargren, Sebastian & Jonéus, Paulina, 2021. "Simulation smoothing for nowcasting with large mixed-frequency VARs," Econometrics and Statistics, Elsevier, vol. 19(C), pages 97-113.
    51. Zens, Gregor & Böck, Maximilian & Zörner, Thomas O., 2020. "The heterogeneous impact of monetary policy on the US labor market," Journal of Economic Dynamics and Control, Elsevier, vol. 119(C).
    52. Zhao, Jing, 2023. "Time-varying impact of geopolitical risk on natural resources prices: Evidence from the hybrid TVP-VAR model with large system," Resources Policy, Elsevier, vol. 82(C).
    53. Saeed Zaman, 2021. "A Unified Framework to Estimate Macroeconomic Stars," Working Papers 21-23R, Federal Reserve Bank of Cleveland, revised 15 Aug 2022.
    54. Mike Tsionas & Marwan Izzeldin & Lorenzo Trapani, 2019. "Bayesian estimation of large dimensional time varying VARs using copulas," Papers 1912.12527, arXiv.org.
    55. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
    56. Juan Antolin-Diaz & Ivan Petrella & Juan F. Rubio-Ramirez, 2021. "Dividend Momentum and Stock Return Predictability: A Bayesian Approach," FRB Atlanta Working Paper 2021-25, Federal Reserve Bank of Atlanta.
    57. Antonio Pacifico, 2021. "Structural Panel Bayesian VAR with Multivariate Time-Varying Volatility to Jointly Deal with Structural Changes, Policy Regime Shifts, and Endogeneity Issues," Econometrics, MDPI, vol. 9(2), pages 1-35, May.
    58. Barbara Rossi, 2019. "Forecasting in the presence of instabilities: How do we know whether models predict well and how to improve them," Economics Working Papers 1711, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2021.
    59. Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano, 2021. "Using time-varying volatility for identification in Vector Autoregressions: An application to endogenous uncertainty," Journal of Econometrics, Elsevier, vol. 225(1), pages 47-73.
    60. Tsionas, Mike, 2022. "Efficiency estimation using probabilistic regression trees with an application to Chilean manufacturing industries," International Journal of Production Economics, Elsevier, vol. 249(C).
    61. Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2022. "Bayesian Forecasting in Economics and Finance: A Modern Review," Papers 2212.03471, arXiv.org, revised Jul 2023.
    62. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    63. Joshua C.C. Chan & Rodney W. Strachan, 2023. "Bayesian State Space Models In Macroeconometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 58-75, February.
    64. Joshua C. C. Chan, 2022. "Asymmetric conjugate priors for large Bayesian VARs," Quantitative Economics, Econometric Society, vol. 13(3), pages 1145-1169, July.
    65. Chen, Zhengyang & Valcarcel, Victor J., 2021. "Monetary transmission in money markets: The not-so-elusive missing piece of the puzzle," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).
    66. Sokol, Andrej, 2021. "Fan charts 2.0: flexible forecast distributions with expert judgement," Working Paper Series 2624, European Central Bank.
    67. Florian Huber & Massimiliano Marcellino, 2023. "Coarsened Bayesian VARs -- Correcting BVARs for Incorrect Specification," Papers 2304.07856, arXiv.org, revised May 2023.
    68. Rub'en Loaiza-Maya & Didier Nibbering, 2022. "Efficient variational approximations for state space models," Papers 2210.11010, arXiv.org, revised Jun 2023.
    69. Florian Huber & Luca Rossini, 2020. "Inference in Bayesian Additive Vector Autoregressive Tree Models," Papers 2006.16333, arXiv.org, revised Mar 2021.
    70. Florian Huber & Gary Koop, 2023. "Subspace shrinkage in conjugate Bayesian vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 556-576, June.
    71. Michael P. Clements & Ana Beatriz Galvão, 2023. "Density forecasting with Bayesian Vector Autoregressive models under macroeconomic data uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 164-185, March.
    72. Chan, Joshua C.C. & Poon, Aubrey & Zhu, Dan, 2023. "High-dimensional conditionally Gaussian state space models with missing data," Journal of Econometrics, Elsevier, vol. 236(1).
    73. Nadja Klein & Michael Stanley Smith & David J. Nott, 2020. "Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices," Papers 2010.01844, arXiv.org, revised May 2021.
    74. Cross, Jamie L. & Hou, Chenghan & Nguyen, Bao H., 2021. "On the China factor in the world oil market: A regime switching approach11We thank Hilde Bjørnland, Tatsuyoshi Okimoto, Ippei Fujiwara, Knut Aastveit, Leif Anders Thorsrud, Francesco Ravazzolo, Renee ," Energy Economics, Elsevier, vol. 95(C).
    75. Arnaud Dufays & Zhuo Li & Jeroen V.K. Rombouts & Yong Song, 2021. "Sparse change‐point VAR models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 703-727, September.
    76. Andrea Carriero & Francesco Corsello & Massimiliano Marcellino, 2020. "The economic drivers of volatility and uncertainty," Temi di discussione (Economic working papers) 1285, Bank of Italy, Economic Research and International Relations Area.
    77. Luis Gruber & Gregor Kastner, 2022. "Forecasting macroeconomic data with Bayesian VARs: Sparse or dense? It depends!," Papers 2206.04902, arXiv.org, revised Jul 2023.
    78. Bognanni, Mark, 2022. "Comment on “Large Bayesian vector autoregressions with stochastic volatility and non-conjugate priors”," Journal of Econometrics, Elsevier, vol. 227(2), pages 498-505.
    79. Antonio Pacifico, 2022. "Structural Compressed Panel VAR with Stochastic Volatility: A Robust Bayesian Model Averaging Procedure," Econometrics, MDPI, vol. 10(3), pages 1-24, July.
    80. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino & Elmar Mertens, 2021. "Forecasting with Shadow-Rate VARs," Working Papers 21-09, Federal Reserve Bank of Cleveland.
    81. Michael Pfarrhofer & Anna Stelzer, 2019. "The international effects of central bank information shocks," Papers 1912.03158, arXiv.org.
    82. Boeck, Maximilian & Feldkircher, Martin, 2021. "The Impact of Monetary Policy on Yield Curve Expectations," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 887-901.
    83. Sebastian Ankargren & Paulina Jon'eus, 2019. "Estimating Large Mixed-Frequency Bayesian VAR Models," Papers 1912.02231, arXiv.org.
    84. Manfred M. Fischer & Niko Hauzenberger & Florian Huber & Michael Pfarrhofer, 2021. "General Bayesian time-varying parameter VARs for predicting government bond yields," Papers 2102.13393, arXiv.org.
    85. Yousuf, Kashif & Ng, Serena, 2021. "Boosting high dimensional predictive regressions with time varying parameters," Journal of Econometrics, Elsevier, vol. 224(1), pages 60-87.
    86. Todd E. Clark & Matthew V. Gordon & Saeed Zaman, 2023. "Forecasting Core Inflation and Its Goods, Housing, and Supercore Components," Working Papers 23-34, Federal Reserve Bank of Cleveland.
    87. Cross, Jamie L. & Hou, Chenghan & Poon, Aubrey, 2020. "Macroeconomic forecasting with large Bayesian VARs: Global-local priors and the illusion of sparsity," International Journal of Forecasting, Elsevier, vol. 36(3), pages 899-915.
    88. Bańbura, Marta & Brenna, Federica & Paredes, Joan & Ravazzolo, Francesco, 2021. "Combining Bayesian VARs with survey density forecasts: does it pay off?," Working Paper Series 2543, European Central Bank.
    89. Nguyen, BH & Zhang, Bo, 2022. "Forecasting oil Prices: can large BVARs help?," Working Papers 2022-04, University of Tasmania, Tasmanian School of Business and Economics.
    90. Kunovac, Davor & Palenzuela, Diego Rodriguez & Sun, Yiqiao, 2022. "A new optimum currency area index for the euro area," Working Paper Series 2730, European Central Bank.
    91. Clements, Michael P. & Galvao, Ana Beatriz, 2020. "Density Forecasting with BVAR Models under Macroeconomic Data Uncertainty," EMF Research Papers 36, Economic Modelling and Forecasting Group.

  18. Claudia Foroni & Massimiliano Marcellino & Dalibor Stevanovic, 2019. "Mixed‐frequency models with moving‐average components," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(5), pages 688-706, August.

    Cited by:

    1. Rachidi Kotchoni & Maxime Leroux & Dalibor Stevanovic, 2019. "Macroeconomic forecast accuracy in a data‐rich environment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1050-1072, November.
    2. Fanelli, Luca & Marsi, Antonio, 2022. "Sovereign spreads and unconventional monetary policy in the Euro area: A tale of three shocks," European Economic Review, Elsevier, vol. 150(C).
    3. Luca Fanelli & Antonio Marsi, 2021. "Unconventional Monetary Policy in the Euro Area: A Tale of Three Shocks," Working Papers wp1164, Dipartimento Scienze Economiche, Universita' di Bologna.

  19. Fabio Bertolotti & Massimiliano Marcellino, 2019. "Tax shocks with high and low uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(6), pages 972-993, September.
    See citations under working paper version above.
  20. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    See citations under working paper version above.
  21. Angela Abbate & Massimiliano Marcellino, 2018. "Point, interval and density forecasts of exchange rates with time varying parameter models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(1), pages 155-179, January.
    See citations under working paper version above.
  22. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2018. "Measuring Uncertainty and Its Impact on the Economy," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 799-815, December.
    See citations under working paper version above.
  23. Lorenzo Bencivelli & Massimiliano Marcellino & Gianluca Moretti, 2017. "Forecasting economic activity by Bayesian bridge model averaging," Empirical Economics, Springer, vol. 53(1), pages 21-40, August.

    Cited by:

    1. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    2. Aprigliano, Valentina & Emiliozzi, Simone & Guaitoli, Gabriele & Luciani, Andrea & Marcucci, Juri & Monteforte, Libero, 2023. "The power of text-based indicators in forecasting Italian economic activity," International Journal of Forecasting, Elsevier, vol. 39(2), pages 791-808.
    3. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    4. José A. Tenreiro Machado & Maria Eugénia Mata & António M. Lopes, 2020. "Fractional Dynamics and Pseudo-Phase Space of Country Economic Processes," Mathematics, MDPI, vol. 8(1), pages 1-17, January.
    5. Stolbov, Mikhail & Shchepeleva, Maria, 2020. "What predicts the legal status of cryptocurrencies?," Economic Analysis and Policy, Elsevier, vol. 67(C), pages 273-291.
    6. Donato Ceci & Andrea Silvestrini, 2023. "Nowcasting the state of the Italian economy: The role of financial markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1569-1593, November.

  24. Knut Are Aastveit & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2017. "Have Standard VARS Remained Stable Since the Crisis?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(5), pages 931-951, August.
    See citations under working paper version above.
  25. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2017. "Explaining the time-varying effects of oil market shocks on US stock returns," Economics Letters, Elsevier, vol. 155(C), pages 84-88.
    See citations under working paper version above.
  26. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2017. "Structural FECM: Cointegration in large‐scale structural FAVAR models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(6), pages 1069-1086, September.
    See citations under working paper version above.
  27. Valentina Aprigliano & Claudia Foroni & Massimiliano Marcellino & Gianluigi Mazzi & Fabrizio Venditti, 2017. "A daily indicator of economic growth for the euro area," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 7(1/2), pages 43-63.

    Cited by:

    1. Dean Fantazzini & Julia Pushchelenko & Alexey Mironenkov & Alexey Kurbatskii, 2021. "Forecasting Internal Migration in Russia Using Google Trends: Evidence from Moscow and Saint Petersburg," Forecasting, MDPI, vol. 3(4), pages 1-30, October.
    2. Tommaso Proietti & Alessandro Giovannelli, 2020. "Nowcasting Monthly GDP with Big Data: a Model Averaging Approach," CEIS Research Paper 482, Tor Vergata University, CEIS, revised 12 May 2020.
    3. Stefan Neuwirth, 2017. "Time-varying mixed frequency forecasting: A real-time experiment," KOF Working papers 17-430, KOF Swiss Economic Institute, ETH Zurich.

  28. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2016. "Common Drifting Volatility in Large Bayesian VARs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 375-390, July.
    See citations under working paper version above.
  29. Guenter W. Beck & Kirstin Hubrich & Massimiliano Marcellino, 2016. "On the Importance of Sectoral and Regional Shocks for Price‐Setting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1234-1253, November.
    See citations under working paper version above.
  30. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2016. "Structural analysis with Multivariate Autoregressive Index models," Journal of Econometrics, Elsevier, vol. 192(2), pages 332-348.
    See citations under working paper version above.
  31. Kapetanios, George & Marcellino, Massimiliano & Papailias, Fotis, 2016. "Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 369-382.

    Cited by:

    1. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    2. Goran Maksimović & Srđan Jović & David Jovović & Marina Jovović, 2019. "RETRACTED ARTICLE: Analyses of Economic Development Based on Different Factors," Computational Economics, Springer;Society for Computational Economics, vol. 53(3), pages 1103-1109, March.
    3. Oguzhan Cepni & I. Ethem Guney & Norman R. Swanson, 2020. "Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 18-36, January.
    4. Đokić, Aleksandar & Jović, Srđan, 2017. "Evaluation of agriculture and industry effect on economic health by ANFIS approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 396-399.
    5. Ausloos, Marcel & Cerqueti, Roy & Bartolacci, Francesca & Castellano, Nicola G., 2018. "SME investment best strategies. Outliers for assessing how to optimize performance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 754-765.
    6. Claveria, Oscar & Monte, Enric & Torra, Salvador, 2020. "Economic forecasting with evolved confidence indicators," Economic Modelling, Elsevier, vol. 93(C), pages 576-585.
    7. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
    8. Oscar Claveria & Enric Monte & Salvador Torra, 2018. "“Tracking economic growth by evolving expectations via genetic programming: A two-step approach”," IREA Working Papers 201801, University of Barcelona, Research Institute of Applied Economics, revised Jan 2018.
    9. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.
    10. Krzysztof DRACHAL, 2020. "Forecasting the Inflation Rate in Poland and U.S. Using Dynamic Model Averaging (DMA) and Google Queries," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 18-34, July.
    11. Emmanuel O. Akande & Elijah O. Akanni & Oyedamola F. Taiwo & Jeremiah D. Joshua & Abel Anthony, 2023. "Predicting inflation component drivers in Nigeria: a stacked ensemble approach," SN Business & Economics, Springer, vol. 3(1), pages 1-32, January.
    12. Oscar Claveria, 2021. "Forecasting with Business and Consumer Survey Data," Forecasting, MDPI, vol. 3(1), pages 1-22, February.
    13. Jović, Srđan & Maksimović, Goran & Jovović, David, 2016. "Appraisal of natural resources rents and economic development," Resources Policy, Elsevier, vol. 50(C), pages 289-291.
    14. Maksimović, Goran & Jović, Srđan & Jovanović, Radomir, 2017. "Economic growth rate management by soft computing approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 520-524.
    15. Tan, Xueping & Sirichand, Kavita & Vivian, Andrew & Wang, Xinyu, 2022. "Forecasting European carbon returns using dimension reduction techniques: Commodity versus financial fundamentals," International Journal of Forecasting, Elsevier, vol. 38(3), pages 944-969.
    16. Petri Kuosmanen & Juuso Vataja, 2017. "The return of financial variables in forecasting GDP growth in the G-7," Economic Change and Restructuring, Springer, vol. 50(3), pages 259-277, August.
    17. Oscar Claveria & Enric Monte & Salvador Torra, 2021. ""Nowcasting and forecasting GDP growth with machine-learning sentiment indicators"," IREA Working Papers 202103, University of Barcelona, Research Institute of Applied Economics, revised Feb 2021.

  32. Georges Kapetanios & Lynda Khalaf & Massimiliano Marcellino, 2016. "Factor‐Based Identification‐Robust Interference in IV Regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(5), pages 821-842, August.

    Cited by:

    1. Omer Bayar, 2022. "Reducing large datasets to improve the identification of estimated policy rules," Empirical Economics, Springer, vol. 63(1), pages 113-140, July.

  33. Marcellino, Massimiliano & Sivec, Vasja, 2016. "Monetary, fiscal and oil shocks: Evidence based on mixed frequency structural FAVARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 335-348.
    See citations under working paper version above.
  34. Angela Abbate & Sandra Eickmeier & Wolfgang Lemke & Massimiliano Marcellino, 2016. "The Changing International Transmission of Financial Shocks: Evidence from a Classical Time‐Varying FAVAR," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(4), pages 573-601, June.
    See citations under working paper version above.
  35. Claudia Foroni & Massimiliano Marcellino, 2016. "Mixed frequency structural vector auto-regressive models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 403-425, February.

    Cited by:

    1. Chudik, Alexander & Georgiadis, Georgios, 2019. "Estimation of impulse response functions when shocks are observed at a higher frequency than outcome variables," Working Paper Series 2307, European Central Bank.
    2. Haroon Mumtaz & Laura Sunder-Plassmann, 2017. "Non-linear effects of government spending shocks in the US. Evidence from state-level data," Working Papers 841, Queen Mary University of London, School of Economics and Finance.
    3. Fanelli, Luca & Marsi, Antonio, 2022. "Sovereign spreads and unconventional monetary policy in the Euro area: A tale of three shocks," European Economic Review, Elsevier, vol. 150(C).
    4. Davtyan, Karen, 2023. "Unconventional monetary policy and economic inequality," Economic Modelling, Elsevier, vol. 126(C).
    5. Camacho, Maximo & Perez-Quiros, Gabriel & Pacce, Matías, 2020. "Spillover effects in international business cycles," Working Paper Series 2484, European Central Bank.
    6. Xin Sheng & Rangan Gupta, 2022. "The State-Level Nonlinear Effects of Government Spending Shocks in the US: The Role of Partisan Conflict," Sustainability, MDPI, vol. 14(18), pages 1-9, September.
    7. Luca Fanelli & Antonio Marsi, 2021. "Unconventional Monetary Policy in the Euro Area: A Tale of Three Shocks," Working Papers wp1164, Dipartimento Scienze Economiche, Universita' di Bologna.
    8. Consolo, Agostino & Foroni, Claudia & Martínez Hernández, Catalina, 2021. "A mixed frequency BVAR for the euro area labour market," Working Paper Series 2601, European Central Bank.

  36. Massimiliano Marcellino & Mario Porqueddu & Fabrizio Venditti, 2016. "Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 118-127, January.
    See citations under working paper version above.
  37. Esteban Prieto & Sandra Eickmeier & Massimiliano Marcellino, 2016. "Time Variation in Macro‐Financial Linkages," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(7), pages 1215-1233, November.
    See citations under working paper version above.
  38. Claudia Foroni & Massimiliano Marcellino & Christian Schumacher, 2015. "Unrestricted mixed data sampling (MIDAS): MIDAS regressions with unrestricted lag polynomials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 178(1), pages 57-82, January.

    Cited by:

    1. Stylianos Asimakopoulos & Joan Paredes & Thomas Warmedinger, 2020. "Real‐Time Fiscal Forecasting Using Mixed‐Frequency Data," Scandinavian Journal of Economics, Wiley Blackwell, vol. 122(1), pages 369-390, January.
    2. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
    3. Harchaoui, Tarek M. & Janssen, Robert V., 2018. "How can big data enhance the timeliness of official statistics?," International Journal of Forecasting, Elsevier, vol. 34(2), pages 225-234.
    4. Holmes, Mark J. & Iregui, Ana María & Otero, Jesús, 2021. "The effects of FX-interventions on forecasters disagreement: A mixed data sampling view," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    5. Edward S. Knotek & Saeed Zaman, 2017. "Financial Nowcasts and Their Usefulness in Macroeconomic Forecasting," Working Papers (Old Series) 1702, Federal Reserve Bank of Cleveland.
    6. Hanan Naser, 2015. "Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods," Empirical Economics, Springer, vol. 49(2), pages 449-479, September.
    7. Fady Barsoum, 2015. "Point and Density Forecasts Using an Unrestricted Mixed-Frequency VAR Model," Working Paper Series of the Department of Economics, University of Konstanz 2015-19, Department of Economics, University of Konstanz.
    8. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
    9. Lima, Luiz Renato & Meng, Fanning & Godeiro, Lucas, 2020. "Quantile forecasting with mixed-frequency data," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1149-1162.
    10. Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2023. "Panel Data Nowcasting: The Case of Price-Earnings Ratios," Papers 2307.02673, arXiv.org.
    11. Tesi Aliaj & Milos Ciganovic & Massimiliano Tancioni, 2023. "Nowcasting inflation with Lasso‐regularized vector autoregressions and mixed frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 464-480, April.
    12. Marie Bessec, 2019. "Revisiting the transitional dynamics of business-cycle phases with mixed-frequency data," Post-Print hal-02181552, HAL.
    13. Qifa Xu & Lu Chen & Cuixia Jiang & Yezheng Liu, 2022. "Forecasting expected shortfall and value at risk with a joint elicitable mixed data sampling model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 407-421, April.
    14. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    15. Matteo Mogliani & Anna Simoni, 2020. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Post-Print hal-03089878, HAL.
    16. Qian Chen & Xiang Gao & Shan Xie & Li Sun & Shuairu Tian & Shigeyuki Hamori, 2021. "On the Predictability of China Macro Indicator with Carbon Emissions Trading," Energies, MDPI, vol. 14(5), pages 1-24, February.
    17. Kyosuke Chikamatsu, Naohisa Hirakata, Yosuke Kido, Kazuki Otaka, 2018. "Nowcasting Japanese GDPs," Bank of Japan Working Paper Series 18-E-18, Bank of Japan.
    18. Sarun Kamolthip, 2021. "Macroeconomic forecasting with LSTM and mixed frequency time series data," Papers 2109.13777, arXiv.org.
    19. Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "News media vs. FRED-MD for macroeconomic forecasting," Working Papers No 08/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    20. David Kohns & Arnab Bhattacharjee, 2020. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," Papers 2011.00938, arXiv.org, revised May 2022.
    21. Ana Beatriz Galvão & Michael Owyang, 2022. "Forecasting low‐frequency macroeconomic events with high‐frequency data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1314-1333, November.
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    3. Niko Hauzenberger, 2020. "Flexible Mixture Priors for Large Time-varying Parameter Models," Papers 2006.10088, arXiv.org, revised Nov 2020.
    4. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    5. Reza Najarzadeh & Alireza Keikha & Hassan Heydari, 2021. "Dynamics of consumption distribution and economic fluctuations," Economic Change and Restructuring, Springer, vol. 54(3), pages 847-876, August.
    6. Guidolin, Massimo & Hansen, Erwin & Pedio, Manuela, 2019. "Cross-asset contagion in the financial crisis: A Bayesian time-varying parameter approach," Journal of Financial Markets, Elsevier, vol. 45(C), pages 83-114.
    7. Angela Abbate & Sandra Eickmeier & Wolfgang Lemke & Massimiliano Marcellino, 2016. "The Changing International Transmission of Financial Shocks: Evidence from a Classical Time‐Varying FAVAR," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(4), pages 573-601, June.
    8. Corradin, Stefano & Grimm, Niklas & Schwaab, Bernd, 2021. "Euro area sovereign bond risk premia during the Covid-19 pandemic," Working Paper Series 2561, European Central Bank.
    9. Karin Klieber, 2023. "Non-linear dimension reduction in factor-augmented vector autoregressions," Papers 2309.04821, arXiv.org.
    10. Marcellino, Massimiliano & Carriero, Andrea & Corsello, Francesco, 2019. "The Global Component of Inflation Volatility," CEPR Discussion Papers 13470, C.E.P.R. Discussion Papers.
    11. Bhattacharya, Rudrani & Chakravarti, Parma & Mundle, Sudipto, 2018. "Forecasting India's Economic Growth: A Time-Varying Parameter Regression Approach," Working Papers 18/238, National Institute of Public Finance and Policy.
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    21. Corradin, Stefano & Schwaab, Bernd, 2023. "Euro area sovereign bond risk premia before and during the Covid-19 pandemic," European Economic Review, Elsevier, vol. 153(C).
    22. Shikha Gupta & Nand Kumar, 2023. "Time varying dynamics of globalization effect in India," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 22(1), pages 81-97, January.
    23. Herwartz, Helmut & Rohloff, Hannes, 2018. "Less bang for the buck? Assessing the role of inflation uncertainty for U.S. monetary policy transmission in a data rich environment," University of Göttingen Working Papers in Economics 358, University of Goettingen, Department of Economics.
    24. Yanhong Feng & Dilong Xu & Pierre Failler & Tinghui Li, 2020. "Research on the Time-Varying Impact of Economic Policy Uncertainty on Crude Oil Price Fluctuation," Sustainability, MDPI, vol. 12(16), pages 1-24, August.
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    26. Yousuf, Kashif & Ng, Serena, 2021. "Boosting high dimensional predictive regressions with time varying parameters," Journal of Econometrics, Elsevier, vol. 224(1), pages 60-87.
    27. Juan S. Holguín & Jorge M. Uribe, 2020. "The credit supply channel of monetary policy: evidence from a FAVAR model with sign restrictions," Empirical Economics, Springer, vol. 59(5), pages 2443-2472, November.
    28. Duván Humberto Cataño & Carlos Vladimir Rodríguez-Caballero & Daniel Peña, 2019. "Wavelet Estimation for Dynamic Factor Models with Time-Varying Loadings," CREATES Research Papers 2019-23, Department of Economics and Business Economics, Aarhus University.
    29. Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
    30. Paolo Gorgi & Siem Jan Koopman & Julia Schaumburg, 2021. "Vector Autoregressions with Dynamic Factor Coefficients and Conditionally Heteroskedastic Errors," Tinbergen Institute Discussion Papers 21-056/III, Tinbergen Institute.

  42. Ferrara, Laurent & Marcellino, Massimiliano & Mogliani, Matteo, 2015. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," International Journal of Forecasting, Elsevier, vol. 31(3), pages 664-679.
    See citations under working paper version above.
  43. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Bayesian VARs: Specification Choices and Forecast Accuracy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 46-73, January.
    See citations under working paper version above.
  44. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2015. "Markov-switching mixed-frequency VAR models," International Journal of Forecasting, Elsevier, vol. 31(3), pages 692-711.
    See citations under working paper version above.
  45. Bulligan, Guido & Marcellino, Massimiliano & Venditti, Fabrizio, 2015. "Forecasting economic activity with targeted predictors," International Journal of Forecasting, Elsevier, vol. 31(1), pages 188-206.

    Cited by:

    1. Caroline Jardet & Baptiste Meunier, 2022. "Nowcasting world GDP growth with high‐frequency data," Post-Print hal-03647097, HAL.
    2. Arabinda Basistha, 2023. "Estimation of short‐run predictive factor for US growth using state employment data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 34-50, January.
    3. Mikosch, Heiner & Solanko, Laura, 2017. "Should one follow movements in the oil price or in money supply? Forecasting quarterly GDP growth in Russia with higher-frequency indicators," BOFIT Discussion Papers 19/2017, Bank of Finland Institute for Emerging Economies (BOFIT).
    4. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    5. Matteo Mogliani & Anna Simoni, 2020. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Post-Print hal-03089878, HAL.
    6. Heiner Mikosch & Laura Solanko, 2019. "Forecasting Quarterly Russian GDP Growth with Mixed-Frequency Data," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 19-35, March.
    7. Kapetanios, George & Marcellino, Massimiliano & Papailias, Fotis, 2016. "Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 369-382.
    8. Sagaert, Yves R. & Kourentzes, Nikolaos & De Vuyst, Stijn & Aghezzaf, El-Houssaine & Desmet, Bram, 2019. "Incorporating macroeconomic leading indicators in tactical capacity planning," International Journal of Production Economics, Elsevier, vol. 209(C), pages 12-19.
    9. Oguzhan Cepni & I. Ethem Guney & Norman R. Swanson, 2020. "Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 18-36, January.
    10. Jokubaitis, Saulius & Celov, Dmitrij & Leipus, Remigijus, 2021. "Sparse structures with LASSO through principal components: Forecasting GDP components in the short-run," International Journal of Forecasting, Elsevier, vol. 37(2), pages 759-776.
    11. Cepni, Oguzhan & Güney, I. Ethem & Swanson, Norman R., 2019. "Nowcasting and forecasting GDP in emerging markets using global financial and macroeconomic diffusion indexes," International Journal of Forecasting, Elsevier, vol. 35(2), pages 555-572.
    12. Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
    13. Risse, Marian, 2019. "Combining wavelet decomposition with machine learning to forecast gold returns," International Journal of Forecasting, Elsevier, vol. 35(2), pages 601-615.
    14. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    15. Ghysels, Eric & Ozkan, Nazire, 2015. "Real-time forecasting of the US federal government budget: A simple mixed frequency data regression approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1009-1020.
    16. Bantis, Evripidis & Clements, Michael P. & Urquhart, Andrew, 2023. "Forecasting GDP growth rates in the United States and Brazil using Google Trends," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1909-1924.
    17. Kutateladze, Varlam, 2022. "The kernel trick for nonlinear factor modeling," International Journal of Forecasting, Elsevier, vol. 38(1), pages 165-177.
    18. Varlam Kutateladze, 2021. "The Kernel Trick for Nonlinear Factor Modeling," Papers 2103.01266, arXiv.org.
    19. Sagaert, Yves R. & Aghezzaf, El-Houssaine & Kourentzes, Nikolaos & Desmet, Bram, 2018. "Tactical sales forecasting using a very large set of macroeconomic indicators," European Journal of Operational Research, Elsevier, vol. 264(2), pages 558-569.
    20. Saulius Jokubaitis & Dmitrij Celov & Remigijus Leipus, 2019. "Sparse structures with LASSO through Principal Components: forecasting GDP components in the short-run," Papers 1906.07992, arXiv.org, revised Oct 2020.
    21. Donato Ceci & Orest Prifti & Andrea Silvestrini, 2024. "Nowcasting Italian GDP growth: a Factor MIDAS approach," Temi di discussione (Economic working papers) 1446, Bank of Italy, Economic Research and International Relations Area.
    22. Nicoletta Pashourtidou & Christos Papamichael & Charalampos Karagiannakis, 2018. "Forecasting economic activity in sectors of the Cypriot economy," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 12(2), pages 24-66, December.
    23. Christiana Anaxagorou & Nicoletta Pashourtidou, 2022. "Forecasting economic activity using preselected predictors: the case of Cyprus," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 16(1), pages 11-36, June.
    24. Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.

  46. Galvao Ana Beatriz & Marcellino Massimiliano, 2014. "The effects of the monetary policy stance on the transmission mechanism," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(3), pages 1-20, May.

    Cited by:

    1. KANAZAWA, Nobuyuki & 金澤, 伸幸, 2018. "Radial Basis Functions Neural Networks for Nonlinear Time Series Analysis and Time-Varying Effects of Supply Shocks," Discussion paper series HIAS-E-64, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    2. Ana B. Galvão & Michael T. Owyang, 2014. "Financial stress regimes and the macroeconomy," Working Papers 2014-20, Federal Reserve Bank of St. Louis.
    3. Martin Bruns & Michele Piffer, 2021. "Monetary policy shocks over the business cycle: Extending the Smooth Transition framework," University of East Anglia School of Economics Working Paper Series 2021-07, School of Economics, University of East Anglia, Norwich, UK..
    4. Elif ERER & Deniz ERER & Mustafa ÇAYIR & Nasuh Oğuzhan ALTAY, 2016. "TCMB, FED ve ECB Para Politikalarının Türkiye Ekonomisi Üzerindeki Etkileri: 1994-2014 Dönemi Analizi," Sosyoekonomi Journal, Sosyoekonomi Society, issue 24(29).

  47. Claudia Foroni & Massimiliano Marcellino, 2014. "Mixed‐Frequency Structural Models: Identification, Estimation, And Policy Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1118-1144, November.

    Cited by:

    1. Marcellino, Massimiliano & Sivec, Vasja, 2016. "Monetary, fiscal and oil shocks: Evidence based on mixed frequency structural FAVARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 335-348.
    2. Laurent Ferrara & Pierre Guérin, 2018. "What are the macroeconomic effects of high-frequency uncertainty shocks?," Post-Print hal-02334586, HAL.
    3. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    4. Martin Feldkircher & Florian Huber & Michael Pfarrhofer, 2020. "Measuring the Effectiveness of US Monetary Policy during the COVID-19 Recession," Papers 2007.15419, arXiv.org.
    5. Bacchiocchi, Emanuele & Bastianin, Andrea & Missale, Alessandro & Rossi, Eduardo, 2016. "Structural analysis with mixed frequencies: monetary policy, uncertainty and gross capital flows," Working Papers 2016-04, Joint Research Centre, European Commission.
    6. Marcellino, Massimiliano & Foroni, Claudia & Casarin, Roberto & Ravazzolo, Francesco, 2017. "Uncertainty Through the Lenses of A Mixed-Frequency Bayesian Panel Markov Switching Model," CEPR Discussion Papers 12339, C.E.P.R. Discussion Papers.
    7. Chambers, Marcus J., 2016. "The estimation of continuous time models with mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 390-404.
    8. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2015. "Testing for Granger causality in large mixed-frequency VARs," Discussion Papers 45/2015, Deutsche Bundesbank.
    9. Bacchiocchi, Emanuele & Bastianin, Andrea & Missale, Alessandro & Rossi, Eduardo, 2020. "Structural analysis with mixed-frequency data: A model of US capital flows," Economic Modelling, Elsevier, vol. 89(C), pages 427-443.
    10. Ghysels, Eric, 2016. "Macroeconomics and the reality of mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 294-314.
    11. Stylianos Asimakopoulos & Marco Lorusso & Francesco Ravazzolo, 2019. "A New Economic Framework: A DSGE Model with Cryptocurrency," Working Papers No 07/2019, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    12. Norberto Rodríguez-Niño & Alejandra Ramírez-Ramírez, 2018. "Metodologías semi-estructurales para estimar la Inflación básica mensual en Colombia," Borradores de Economia 1040, Banco de la Republica de Colombia.
    13. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    14. Claudia Foroni & Massimiliano Marcellino, 2014. "Mixed frequency structural VARs," Working Paper 2014/01, Norges Bank.
    15. Bent Jesper Christensen & Olaf Posch & Michel van der Wel, 2014. "Estimating Dynamic Equilibrium Models Using Mixed Frequency Macro and Financial Data," CESifo Working Paper Series 5030, CESifo.
    16. Ruey Yau & C. James Hueng, 2019. "Nowcasting GDP Growth for Small Open Economies with a Mixed-Frequency Structural Model," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 177-198, June.

  48. Banerjee, Anindya & Marcellino, Massimiliano & Masten, Igor, 2014. "Forecasting with factor-augmented error correction models," International Journal of Forecasting, Elsevier, vol. 30(3), pages 589-612.
    See citations under working paper version above.
  49. Ghysels, Eric & Guérin, Pierre & Marcellino, Massimiliano, 2014. "Regime switches in the risk–return trade-off," Journal of Empirical Finance, Elsevier, vol. 28(C), pages 118-138.
    See citations under working paper version above.
  50. Foroni, Claudia & Marcellino, Massimiliano, 2014. "A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates," International Journal of Forecasting, Elsevier, vol. 30(3), pages 554-568.

    Cited by:

    1. Christiane Baumeister & Pierre Guérin, 2020. "A Comparison of Monthly Global Indicators for Forecasting Growth," CESifo Working Paper Series 8656, CESifo.
    2. Markus Heinrich & Magnus Reif, 2018. "Forecasting using mixed-frequency VARs with time-varying parameters," ifo Working Paper Series 273, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    3. Mikosch, Heiner & Solanko, Laura, 2017. "Should one follow movements in the oil price or in money supply? Forecasting quarterly GDP growth in Russia with higher-frequency indicators," BOFIT Discussion Papers 19/2017, Bank of Finland Institute for Emerging Economies (BOFIT).
    4. Schwarzmüller, Tim, 2015. "Model pooling and changes in the informational content of predictors: An empirical investigation for the euro area," Kiel Working Papers 1982, Kiel Institute for the World Economy (IfW Kiel).
    5. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    6. Takashi Nakazawa, 2022. "Constructing GDP Nowcasting Models Using Alternative Data," Bank of Japan Working Paper Series 22-E-9, Bank of Japan.
    7. Kyosuke Chikamatsu, Naohisa Hirakata, Yosuke Kido, Kazuki Otaka, 2018. "Nowcasting Japanese GDPs," Bank of Japan Working Paper Series 18-E-18, Bank of Japan.
    8. David Kohns & Arnab Bhattacharjee, 2020. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," Papers 2011.00938, arXiv.org, revised May 2022.
    9. Zhang, Yue-Jun & Wang, Jin-Li, 2019. "Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models," Energy Economics, Elsevier, vol. 78(C), pages 192-201.
    10. David Kohns & Arnab Bhattacharjee, 2019. "Interpreting Big Data in the Macro Economy: A Bayesian Mixed Frequency Estimator," CEERP Working Paper Series 010, Centre for Energy Economics Research and Policy, Heriot-Watt University.
    11. Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.
    12. Heiner Mikosch & Laura Solanko, 2019. "Forecasting Quarterly Russian GDP Growth with Mixed-Frequency Data," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 19-35, March.
    13. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    14. Kapetanios, George & Marcellino, Massimiliano & Papailias, Fotis, 2016. "Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 369-382.
    15. Tommaso Proietti & Alessandro Giovannelli, 2020. "Nowcasting Monthly GDP with Big Data: a Model Averaging Approach," CEIS Research Paper 482, Tor Vergata University, CEIS, revised 12 May 2020.
    16. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
    17. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    18. Christian Glocker & Serguei Kaniovski, 2022. "Macroeconometric forecasting using a cluster of dynamic factor models," Empirical Economics, Springer, vol. 63(1), pages 43-91, July.
    19. Gani Ramadani & Magdalena Petrovska & Vesna Bucevska, 2021. "Evaluation of mixed frequency approaches for tracking near-term economic developments in North Macedonia," Working Papers 2021-03, National Bank of the Republic of North Macedonia.
    20. Dennis Kant & Andreas Pick & Jasper de Winter, 2022. "Nowcasting GDP using machine learning methods," Working Papers 754, DNB.
    21. Dr. Alain Galli & Dr. Christian Hepenstrick & Dr. Rolf Scheufele, 2017. "Mixed-frequency models for tracking short-term economic developments in Switzerland," Working Papers 2017-02, Swiss National Bank.
    22. Cepni, Oguzhan & Güney, I. Ethem & Swanson, Norman R., 2019. "Nowcasting and forecasting GDP in emerging markets using global financial and macroeconomic diffusion indexes," International Journal of Forecasting, Elsevier, vol. 35(2), pages 555-572.
    23. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2014. "Combined Density Nowcasting in an uncertain economic environment," Working Paper 2014/17, Norges Bank.
    24. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    25. Alejo Estavillo & Gabriela Mordecki, 2023. "Nowcasting del PIB para Uruguay en base a un modelo de ecuaciones puente," Documentos de Trabajo (working papers) 23-26, Instituto de Economía - IECON.
    26. Heinisch Katja & Scheufele Rolf, 2019. "Should Forecasters Use Real-Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence," German Economic Review, De Gruyter, vol. 20(4), pages 170-200, December.
    27. Pirschel, Inske, 2016. "Forecasting euro area recessions in real-time," Kiel Working Papers 2020, Kiel Institute for the World Economy (IfW Kiel).
    28. Luke Mosley & Tak-Shing Chan & Alex Gibberd, 2023. "sparseDFM: An R Package to Estimate Dynamic Factor Models with Sparse Loadings," Papers 2303.14125, arXiv.org.
    29. Garciga, Christian & Knotek II, Edward S., 2019. "Forecasting GDP growth with NIPA aggregates: In search of core GDP," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1814-1828.
    30. Proietti, Tommaso & Giovannelli, Alessandro & Ricchi, Ottavio & Citton, Ambra & Tegami, Christían & Tinti, Cristina, 2021. "Nowcasting GDP and its components in a data-rich environment: The merits of the indirect approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1376-1398.
    31. Pérez Quirós, Gabriel & Pérez, Javier J. & Paredes, Joan, 2015. "Fiscal targets. A guide to forecasters?," Working Paper Series 1834, European Central Bank.
    32. Rudrani Bhattacharya & Bornali Bhandari & Sudipto Mundle, 2023. "Nowcasting India’s Quarterly GDP Growth: A Factor-Augmented Time-Varying Coefficient Regression Model (FA-TVCRM)," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(1), pages 213-234, March.
    33. Jack Fosten & Daniel Gutknecht, 2021. "Horizon confidence sets," Empirical Economics, Springer, vol. 61(2), pages 667-692, August.
    34. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2020. "Regional output growth in the United Kingdom: More timely and higher frequency estimates from 1970," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(2), pages 176-197, March.
    35. Ramadani Gani & Petrovska Magdalena & Bucevska Vesna, 2021. "Evaluation of Mixed Frequency Approaches for Tracking Near-Term Economic Developments in North Macedonia," South East European Journal of Economics and Business, Sciendo, vol. 16(2), pages 43-52, December.
    36. Michal Franta & David Havrlant & Marek Rusnak, 2014. "Forecasting Czech GDP Using Mixed-Frequency Data Models," Working Papers 2014/08, Czech National Bank.
    37. Hwee Kwan Chow & Yijie Fei & Daniel Han, 2023. "Forecasting GDP with many predictors in a small open economy: forecast or information pooling?," Empirical Economics, Springer, vol. 65(2), pages 805-829, August.
    38. Deschamps, Bruno & Ioannidis, Christos & Ka, Kook, 2020. "High-frequency credit spread information and macroeconomic forecast revision," International Journal of Forecasting, Elsevier, vol. 36(2), pages 358-372.
    39. Markus Heinrich & Magnus Reif, 2020. "Real-Time Forecasting Using Mixed-Frequency VARS with Time-Varying Parameters," CESifo Working Paper Series 8054, CESifo.
    40. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    41. Algaba, Andres & Borms, Samuel & Boudt, Kris & Verbeken, Brecht, 2023. "Daily news sentiment and monthly surveys: A mixed-frequency dynamic factor model for nowcasting consumer confidence," International Journal of Forecasting, Elsevier, vol. 39(1), pages 266-278.
    42. Kenichiro McAlinn, 2021. "Mixed‐frequency Bayesian predictive synthesis for economic nowcasting," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1143-1163, November.
    43. Hassani, Hossein & Rua, António & Silva, Emmanuel Sirimal & Thomakos, Dimitrios, 2019. "Monthly forecasting of GDP with mixed-frequency multivariate singular spectrum analysis," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1263-1272.
    44. Saiz, Lorena & Ashwin, Julian & Kalamara, Eleni, 2021. "Nowcasting euro area GDP with news sentiment: a tale of two crises," Working Paper Series 2616, European Central Bank.
    45. Yang, Jianlei & Yang, Chunpeng, 2021. "The impact of mixed-frequency geopolitical risk on stock market returns," Economic Analysis and Policy, Elsevier, vol. 72(C), pages 226-240.
    46. Pinkwart, Nicolas, 2018. "Short-term forecasting economic activity in Germany: A supply and demand side system of bridge equations," Discussion Papers 36/2018, Deutsche Bundesbank.
    47. Bantis, Evripidis & Clements, Michael P. & Urquhart, Andrew, 2023. "Forecasting GDP growth rates in the United States and Brazil using Google Trends," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1909-1924.
    48. Alain Hecq & Marie Ternes & Ines Wilms, 2021. "Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions," Papers 2102.11780, arXiv.org, revised Mar 2022.
    49. Xianning WANG & Jingrong DONG & Zhi XIAO & Guanjie HE, 2019. "A novel spatial mixed frequency forecasting model with application to Chinese regional GDP," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 54-77, June.
    50. Fornaro, Paolo, 2016. "Predicting Finnish economic activity using firm-level data," International Journal of Forecasting, Elsevier, vol. 32(1), pages 10-19.
    51. Alejandro Fernández Cerezo, 2023. "A supply-side GDP nowcasting model," Economic Bulletin, Banco de España, issue 2023/Q1.
    52. Jos Jansen, W. & Jin, Xiaowen & Winter, Jasper M. de, 2016. "Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts," Munich Reprints in Economics 43488, University of Munich, Department of Economics.
    53. Stavros Degiannakis, 2023. "The D-model for GDP nowcasting," Working Papers 317, Bank of Greece.
    54. Daniel Borup & David E. Rapach & Erik Christian Montes Schütte, 2021. "Now- and Backcasting Initial Claims with High-Dimensional Daily Internet Search-Volume Data," CREATES Research Papers 2021-02, Department of Economics and Business Economics, Aarhus University.
    55. Cláudia Duarte, 2016. "A Mixed Frequency Approach to Forecast Private Consumption with ATM/POS Data," Working Papers w201601, Banco de Portugal, Economics and Research Department.
    56. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    57. Serena Ng & Susannah Scanlan, 2023. "Constructing High Frequency Economic Indicators by Imputation," Papers 2303.01863, arXiv.org, revised Oct 2023.
    58. Gong, Xu & Sun, Yi & Du, Zhili, 2022. "Geopolitical risk and China's oil security," Energy Policy, Elsevier, vol. 163(C).
    59. Kohns, David & Bhattacharjee, Arnab, 2023. "Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1384-1412.
    60. Chikamatsu, Kyosuke & Hirakata, Naohisa & Kido, Yosuke & Otaka, Kazuki, 2021. "Mixed-frequency approaches to nowcasting GDP: An application to Japan," Japan and the World Economy, Elsevier, vol. 57(C).
    61. Alain Hecq & Marie Ternes & Ines Wilms, 2023. "Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions," Papers 2301.10592, arXiv.org.
    62. Donato Ceci & Orest Prifti & Andrea Silvestrini, 2024. "Nowcasting Italian GDP growth: a Factor MIDAS approach," Temi di discussione (Economic working papers) 1446, Bank of Italy, Economic Research and International Relations Area.
    63. Nicoletta Pashourtidou & Christos Papamichael & Charalampos Karagiannakis, 2018. "Forecasting economic activity in sectors of the Cypriot economy," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 12(2), pages 24-66, December.
    64. Alessandro Giovannelli & Marco Lippi & Tommaso Proietti, 2023. "Band-Pass Filtering with High-Dimensional Time Series," CEIS Research Paper 559, Tor Vergata University, CEIS, revised 15 Jun 2023.
    65. Konstantin Kuck & Karsten Schweikert, 2021. "Forecasting Baden‐Württemberg's GDP growth: MIDAS regressions versus dynamic mixed‐frequency factor models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 861-882, August.
    66. Heinisch, Katja, 2016. "A real-time analysis on the importance of hard and soft data for nowcasting German GDP," VfS Annual Conference 2016 (Augsburg): Demographic Change 145864, Verein für Socialpolitik / German Economic Association.
    67. Christiana Anaxagorou & Nicoletta Pashourtidou, 2022. "Forecasting economic activity using preselected predictors: the case of Cyprus," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 16(1), pages 11-36, June.
    68. Barış Soybilgen & M. Ege Yazgan & Hüseyin Kaya, 2023. "Nowcasting Turkish Food Inflation Using Daily Online Prices," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 19(2), pages 171-190, September.
    69. Gary Koop & Stuart McIntyre & James Mitchell, 2020. "UK regional nowcasting using a mixed frequency vector auto‐regressive model with entropic tilting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 91-119, January.
    70. Pirschel, Inske, 2015. "Forecasting Euro Area Recessions in real-time with a mixed-frequency Bayesian VAR," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113031, Verein für Socialpolitik / German Economic Association.
    71. Kohns, David & Potjagailo, Galina, 2023. "Flexible Bayesian MIDAS: time‑variation, group‑shrinkage and sparsity," Bank of England working papers 1025, Bank of England.
    72. Ruey Yau & C. James Hueng, 2019. "Nowcasting GDP Growth for Small Open Economies with a Mixed-Frequency Structural Model," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 177-198, June.
    73. Mahmut Gunay, 2020. "Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial," Working Papers 2002, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    74. Borup, Daniel & Rapach, David E. & Schütte, Erik Christian Montes, 2023. "Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1122-1144.

  51. Massimiliano Marcellino & Yuliya Rychalovska, 2014. "Forecasting with a DSGE Model of a Small Open Economy within the Monetary Union," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(5), pages 315-338, August.

    Cited by:

    1. Ginters Bušs & Patrick Grüning, 2023. "Fiscal DSGE model for Latvia," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 23(1), pages 2173915-217.
    2. Marcin Kolasa & Michał Rubaszek, 2018. "Does the foreign sector help forecast domestic variables in DSGE models?," NBP Working Papers 282, Narodowy Bank Polski.
    3. Roberta Cardani & Alessia Paccagnini & Stefania Villa, 2019. "Forecasting with instabilities: an application to DSGE models with financial frictions," Temi di discussione (Economic working papers) 1234, Bank of Italy, Economic Research and International Relations Area.
    4. Erlan Konebayev, 2023. "Forecasting a Commodity-Exporting Small Open Developing Economy Using DSGE and DSGE-BVAR," International Economic Journal, Taylor & Francis Journals, vol. 37(1), pages 39-70, January.
    5. Stelios D. Bekiros & Alessia Paccagnini, 2014. "Policy-oriented macroeconomic forecasting with hybrid DGSE and time-varying parameter VAR models," Working Papers 2014-426, Department of Research, Ipag Business School.
    6. Van Nguyen, Phuong, 2020. "Evaluating the forecasting accuracy of the closed- and open economy New Keynesian DSGE models," Dynare Working Papers 59, CEPREMAP.
    7. David Alan Peel & Pantelis Promponas, 2016. "Forecasting the nominal exchange rate movements in a changing world. The case of the U.S. and the U.K," Working Papers 144439514, Lancaster University Management School, Economics Department.
    8. Roberto Duncan & Enrique Martínez‐García, 2023. "Forecasting inflation in open economies: What can a NOEM model do?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 481-513, April.
    9. Roberta Cardani & Alessia Paccagnini & Stefania Villa, 2015. "Forecasting in a DSGE Model with Banking Intermediation: Evidence from the US," Working Papers 292, University of Milano-Bicocca, Department of Economics, revised Feb 2015.
    10. Fernando de Menezes Linardi, 2016. "Assessing the Fit of a Small Open-Economy DSGE Model for the Brazilian Economy," Working Papers Series 424, Central Bank of Brazil, Research Department.

  52. Bekiros, Stelios & Marcellino, Massimiliano, 2013. "The multiscale causal dynamics of foreign exchange markets," Journal of International Money and Finance, Elsevier, vol. 33(C), pages 282-305.
    See citations under working paper version above.
  53. Vladimir Kuzin & Massimiliano Marcellino & Christian Schumacher, 2013. "Pooling Versus Model Selection For Nowcasting Gdp With Many Predictors: Empirical Evidence For Six Industrialized Countries," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(3), pages 392-411, April.

    Cited by:

    1. Robert Lehmann, 2016. "Economic Growth and Business Cycle Forecasting at the Regional Level," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65.
    2. Luci Alessi & Eric Ghysels & Luca Onorante & Richard Peach & Simon M. Potter, 2014. "Central bank macroeconomic forecasting during the global financial crisis: the European Central Bank and Federal Reserve Bank of New York experiences," Staff Reports 680, Federal Reserve Bank of New York.
    3. Inske Pirschel & Maik H. Wolters, 2018. "Forecasting with large datasets: compressing information before, during or after the estimation?," Empirical Economics, Springer, vol. 55(2), pages 573-596, September.
    4. Fady Barsoum, 2015. "Point and Density Forecasts Using an Unrestricted Mixed-Frequency VAR Model," Working Paper Series of the Department of Economics, University of Konstanz 2015-19, Department of Economics, University of Konstanz.
    5. Mikosch, Heiner & Solanko, Laura, 2017. "Should one follow movements in the oil price or in money supply? Forecasting quarterly GDP growth in Russia with higher-frequency indicators," BOFIT Discussion Papers 19/2017, Bank of Finland Institute for Emerging Economies (BOFIT).
    6. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
    7. Reichlin, Lucrezia & Giannone, Domenico & Modugno, Michele & Banbura, Marta, 2012. "Now-casting and the real-time data flow," CEPR Discussion Papers 9112, C.E.P.R. Discussion Papers.
    8. Schwarzmüller, Tim, 2015. "Model pooling and changes in the informational content of predictors: An empirical investigation for the euro area," Kiel Working Papers 1982, Kiel Institute for the World Economy (IfW Kiel).
    9. A. Girardi & R. Golinelli & C. Pappalardo, 2014. "The Role of Indicator Selection in Nowcasting Euro Area GDP in Pseudo Real Time," Working Papers wp919, Dipartimento Scienze Economiche, Universita' di Bologna.
    10. Takashi Nakazawa, 2022. "Constructing GDP Nowcasting Models Using Alternative Data," Bank of Japan Working Paper Series 22-E-9, Bank of Japan.
    11. Kyosuke Chikamatsu, Naohisa Hirakata, Yosuke Kido, Kazuki Otaka, 2018. "Nowcasting Japanese GDPs," Bank of Japan Working Paper Series 18-E-18, Bank of Japan.
    12. Ana Beatriz Galvão & Michael Owyang, 2022. "Forecasting low‐frequency macroeconomic events with high‐frequency data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1314-1333, November.
    13. Heiner Mikosch & Laura Solanko, 2019. "Forecasting Quarterly Russian GDP Growth with Mixed-Frequency Data," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 19-35, March.
    14. Hwee Kwan Chow & Keen Meng Choy, 2023. "Economic forecasting in a pandemic: some evidence from Singapore," Empirical Economics, Springer, vol. 64(5), pages 2105-2124, May.
    15. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    16. Aleksandra Riedl & Julia Wörz, 2018. "A simple approach to nowcasting GDP growth in CESEE economies," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue Q4/18, pages 56-74.
    17. Andrea Carriero & Galvao, Ana Beatriz & Kapetanios, George, 2016. "A comprehensive evaluation of macroeconomic forecasting methods," EMF Research Papers 10, Economic Modelling and Forecasting Group.
    18. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    19. Tommaso Proietti & Alessandro Giovannelli, 2020. "Nowcasting Monthly GDP with Big Data: a Model Averaging Approach," CEIS Research Paper 482, Tor Vergata University, CEIS, revised 12 May 2020.
    20. Alexander Chudik & Valerie Grossman & M. Hashem Pesaran, 2014. "A multi-country approach to forecasting output growth using PMIs," Globalization Institute Working Papers 213, Federal Reserve Bank of Dallas.
    21. Dorinth van Dijk & Jasper de Winter, 2023. "Nowcasting GDP using tone-adjusted time varying news topics: Evidence from the financial press," Working Papers 766, DNB.
    22. George Kapetanios & Fotis Papailias, 2018. "Big Data & Macroeconomic Nowcasting: Methodological Review," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-12, Economic Statistics Centre of Excellence (ESCoE).
    23. Zhao, Xin & Han, Meng & Ding, Lili & Kang, Wanglin, 2018. "Usefulness of economic and energy data at different frequencies for carbon price forecasting in the EU ETS," Applied Energy, Elsevier, vol. 216(C), pages 132-141.
    24. Daniel Kaufmann & Rolf Scheufele, 2015. "Business tendency surveys and macroeconomic fluctuations," KOF Working papers 15-378, KOF Swiss Economic Institute, ETH Zurich.
    25. Soybilgen, Barış & Yazgan, Ege, 2018. "Evaluating nowcasts of bridge equations with advanced combination schemes for the Turkish unemployment rate," Economic Modelling, Elsevier, vol. 72(C), pages 99-108.
    26. Christian Glocker & Serguei Kaniovski, 2022. "Macroeconometric forecasting using a cluster of dynamic factor models," Empirical Economics, Springer, vol. 63(1), pages 43-91, July.
    27. Han, Meng & Ding, Lili & Zhao, Xin & Kang, Wanglin, 2019. "Forecasting carbon prices in the Shenzhen market, China: The role of mixed-frequency factors," Energy, Elsevier, vol. 171(C), pages 69-76.
    28. Hauber, Philipp, 2018. "Zur Kurzfristprognose mit Faktormodellen und Prognoseanpassungen," Kiel Insight 2018.5, Kiel Institute for the World Economy (IfW Kiel).
    29. Dennis Kant & Andreas Pick & Jasper de Winter, 2022. "Nowcasting GDP using machine learning methods," Working Papers 754, DNB.
    30. Barış Soybilgen & Ege Yazgan, 2021. "Nowcasting US GDP Using Tree-Based Ensemble Models and Dynamic Factors," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 387-417, January.
    31. Cepni, Oguzhan & Güney, I. Ethem & Swanson, Norman R., 2019. "Nowcasting and forecasting GDP in emerging markets using global financial and macroeconomic diffusion indexes," International Journal of Forecasting, Elsevier, vol. 35(2), pages 555-572.
    32. Knut Are Aastveit & Francesco Ravazzolo & Herman K. van Dijk, 2014. "Combined Density Nowcasting in an uncertain economic environment," Working Paper 2014/17, Norges Bank.
    33. Robert Lehmann & Klaus Wohlrabe, 2012. "Forecasting GDP at the Regional Level with Many Predictors," CESifo Working Paper Series 3956, CESifo.
    34. Maxime Leboeuf & Louis Morel, 2014. "Forecasting Short-Term Real GDP Growth in the Euro Area and Japan Using Unrestricted MIDAS Regressions," Discussion Papers 14-3, Bank of Canada.
    35. Luke Mosley & Idris A. Eckley & Alex Gibberd, 2022. "Sparse temporal disaggregation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2203-2233, October.
    36. Barış Soybilgen & Ege Yazgan, 2018. "Nowcasting the New Turkish GDP," Economics Bulletin, AccessEcon, vol. 38(2), pages 1083-1089.
    37. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2014. "Density forecasts with MIDAS models," Working Paper 2014/10, Norges Bank.
    38. Jack Fosten & Daniel Gutknecht, 2021. "Horizon confidence sets," Empirical Economics, Springer, vol. 61(2), pages 667-692, August.
    39. Marek Rusnak, 2013. "Nowcasting Czech GDP in Real Time," Working Papers 2013/06, Czech National Bank.
    40. Çepni, Oğuzhan & Guney, I. Ethem & Gupta, Rangan & Wohar, Mark E., 2020. "The role of an aligned investor sentiment index in predicting bond risk premia of the U.S," Journal of Financial Markets, Elsevier, vol. 51(C).
    41. Hwee Kwan Chow & Yijie Fei & Daniel Han, 2023. "Forecasting GDP with many predictors in a small open economy: forecast or information pooling?," Empirical Economics, Springer, vol. 65(2), pages 805-829, August.
    42. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    43. Frédérique Bec & Matteo Mogliani, 2013. "Nowcasting French GDP in Real-Time from Survey Opinions : Information or Forecast Combinations ?," Working Papers 2013-21, Center for Research in Economics and Statistics.
    44. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
    45. Irma Hindrayanto & Siem Jan Koopman & Jasper de Winter, 2014. "Nowcasting and Forecasting Economic Growth in the Euro Area using Principal Components," Tinbergen Institute Discussion Papers 14-113/III, Tinbergen Institute.
    46. Ghysels, Eric & Ozkan, Nazire, 2015. "Real-time forecasting of the US federal government budget: A simple mixed frequency data regression approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1009-1020.
    47. Davide Pettenuzzo & Rossen Valkanov & Allan Timmermann, 2014. "A Bayesian MIDAS Approach to Modeling First and Second Moment Dynamics," Working Papers 76, Brandeis University, Department of Economics and International Business School.
    48. Pettenuzzo, Davide & Timmermann, Allan & Valkanov, Rossen, 2016. "A MIDAS approach to modeling first and second moment dynamics," Journal of Econometrics, Elsevier, vol. 193(2), pages 315-334.
    49. Marcellino, Massimiliano & Foroni, Claudia & Stevanovic, Dalibor, 2020. "Forecasting the Covid-19 recession and recovery: Lessons from the financial crisis," CEPR Discussion Papers 15114, C.E.P.R. Discussion Papers.
    50. Hindrayanto, Irma & Koopman, Siem Jan & de Winter, Jasper, 2016. "Forecasting and nowcasting economic growth in the euro area using factor models," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1284-1305.
    51. Pinkwart, Nicolas, 2018. "Short-term forecasting economic activity in Germany: A supply and demand side system of bridge equations," Discussion Papers 36/2018, Deutsche Bundesbank.
    52. Xianning WANG & Jingrong DONG & Zhi XIAO & Guanjie HE, 2019. "A novel spatial mixed frequency forecasting model with application to Chinese regional GDP," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 54-77, June.
    53. Foroni, Claudia & Marcellino, Massimiliano, 2014. "A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates," International Journal of Forecasting, Elsevier, vol. 30(3), pages 554-568.
    54. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
    55. Oguzhan Cepni & Rangan Gupta & Yigit Onay, 2022. "The role of investor sentiment in forecasting housing returns in China: A machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1725-1740, December.
    56. Jos Jansen, W. & Jin, Xiaowen & Winter, Jasper M. de, 2016. "Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts," Munich Reprints in Economics 43488, University of Munich, Department of Economics.
    57. Schumacher, Christian, 2014. "MIDAS regressions with time-varying parameters: An application to corporate bond spreads and GDP in the Euro area," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100289, Verein für Socialpolitik / German Economic Association.
    58. Tony Chernis & Rodrigo Sekkel, 2018. "Nowcasting Canadian Economic Activity in an Uncertain Environment," Discussion Papers 18-9, Bank of Canada.
    59. Chikamatsu, Kyosuke & Hirakata, Naohisa & Kido, Yosuke & Otaka, Kazuki, 2021. "Mixed-frequency approaches to nowcasting GDP: An application to Japan," Japan and the World Economy, Elsevier, vol. 57(C).
    60. Ryan T. Ball & Eric Ghysels, 2018. "Automated Earnings Forecasts: Beat Analysts or Combine and Conquer?," Management Science, INFORMS, vol. 64(10), pages 4936-4952, October.
    61. Cristea, R. G., 2020. "Can Alternative Data Improve the Accuracy of Dynamic Factor Model Nowcasts?," Cambridge Working Papers in Economics 20108, Faculty of Economics, University of Cambridge.
    62. Pirschel, Inske & Wolters, Maik, 2014. "Forecasting German key macroeconomic variables using large dataset methods," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100587, Verein für Socialpolitik / German Economic Association.
    63. Nikoleta Anesti & Ana Beatriz Galvão & Silvia Miranda‐Agrippino, 2022. "Uncertain Kingdom: Nowcasting Gross Domestic Product and its revisions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 42-62, January.
    64. Alessandro Giovannelli & Marco Lippi & Tommaso Proietti, 2023. "Band-Pass Filtering with High-Dimensional Time Series," CEIS Research Paper 559, Tor Vergata University, CEIS, revised 15 Jun 2023.
    65. Konstantin Kuck & Karsten Schweikert, 2021. "Forecasting Baden‐Württemberg's GDP growth: MIDAS regressions versus dynamic mixed‐frequency factor models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 861-882, August.
    66. Claudia Foroni & Massimiliano Marcellino, 2014. "Mixed frequency structural VARs," Working Paper 2014/01, Norges Bank.
    67. Ademmer, Martin & Boysen-Hogrefe, Jens & Fiedler, Salomon & Groll, Dominik & Hauber, Philipp & Jannsen, Nils & Kooths, Stefan & Potjagailo, Galina, 2018. "Deutsche Konjunktur im Frühjahr 2018 - Deutsche Wirtschaft näher am Limit [German Economy Spring 2018 - German economy closer to its limit]," Kieler Konjunkturberichte 41, Kiel Institute for the World Economy (IfW Kiel).
    68. Byron Botha & Geordie Reid & Tim Olds & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African GDP using a suite of statistical models," Working Papers 11001, South African Reserve Bank.
    69. Luke Mosley & Idris Eckley & Alex Gibberd, 2021. "Sparse Temporal Disaggregation," Papers 2108.05783, arXiv.org, revised Oct 2022.
    70. Mahmut Gunay, 2020. "Nowcasting Turkish GDP with MIDAS: Role of Functional Form of the Lag Polynomial," Working Papers 2002, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    71. Ard Reijer & Andreas Johansson, 2019. "Nowcasting Swedish GDP with a large and unbalanced data set," Empirical Economics, Springer, vol. 57(4), pages 1351-1373, October.
    72. Byron Botha & Tim Olds & Geordie Reid & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African gross domestic product using a suite of statistical models," South African Journal of Economics, Economic Society of South Africa, vol. 89(4), pages 526-554, December.

  54. Jordà, Òscar & Knüppel, Malte & Marcellino, Massimiliano, 2013. "Empirical simultaneous prediction regions for path-forecasts," International Journal of Forecasting, Elsevier, vol. 29(3), pages 456-468.
    See citations under working paper version above.
  55. Pierre Guérin & Massimiliano Marcellino, 2013. "Markov-Switching MIDAS Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(1), pages 45-56, January.
    See citations under working paper version above.
  56. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2012. "Forecasting government bond yields with large Bayesian vector autoregressions," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2026-2047.

    Cited by:

    1. Alberto Caruso & Laura Coroneo, 2023. "Does Real‐Time Macroeconomic Information Help to Predict Interest Rates?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(8), pages 2027-2059, December.
    2. Gefang, Deborah & Koop, Gary & Poon, Aubrey, 2023. "Forecasting using variational Bayesian inference in large vector autoregressions with hierarchical shrinkage," International Journal of Forecasting, Elsevier, vol. 39(1), pages 346-363.
    3. Florian Huber & Gregor Kastner & Martin Feldkircher, 2016. "Should I stay or should I go? A latent threshold approach to large-scale mixture innovation models," Papers 1607.04532, arXiv.org, revised Jul 2018.
    4. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
    5. Deborah Gefang & Gary Koop & Aubrey Poon, 2019. "Variational Bayesian inference in large Vector Autoregressions with hierarchical shrinkage," CAMA Working Papers 2019-08, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    6. Brave, Scott A. & Butters, R. Andrew & Justiniano, Alejandro, 2019. "Forecasting economic activity with mixed frequency BVARs," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1692-1707.
    7. Joseph P. Byrne & Shuo Cao. & Dimitris Korobilis., 2015. "Term Structure Dynamics, Macro-Finance Factors and Model Uncertainty," Working Papers 2015_08, Business School - Economics, University of Glasgow.
    8. Joshua C.C. Chan & Eric Eisenstat & Chenghan Hou & Gary Koop, 2018. "Composite likelihood methods for large Bayesian VARs with stochastic volatility," CAMA Working Papers 2018-26, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    9. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2014. "No Arbitrage Priors, Drifting Volatilities, and the Term Structure of Interest Rates," CEPR Discussion Papers 9848, C.E.P.R. Discussion Papers.
    10. Banerjee, Anindya & Marcellino, Massimiliano, 2008. "Factor-augmented Error Correction Models," CEPR Discussion Papers 6707, C.E.P.R. Discussion Papers.
    11. Dimitris Korobilis & Davide Pettenuzzo, 2017. "Adaptive Hierarchical Priors for High-Dimensional Vector Autoregessions," Working Papers 115, Brandeis University, Department of Economics and International Business School.
    12. Dimitris P. Louzis, 2014. "Macroeconomic and credit forecasts in a small economy during crisis: A large Bayesian VAR approach," Working Papers 184, Bank of Greece.
    13. Ranik Raaen Wahlstrøm & Florentina Paraschiv & Michael Schürle, 2022. "A Comparative Analysis of Parsimonious Yield Curve Models with Focus on the Nelson-Siegel, Svensson and Bliss Versions," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 967-1004, March.
    14. Helmut Lütkepohl, 2014. "Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey," SFB 649 Discussion Papers SFB649DP2014-004, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    15. Guanhao Feng & Nicholas Polson, 2020. "Regularizing Bayesian predictive regressions," Journal of Asset Management, Palgrave Macmillan, vol. 21(7), pages 591-608, December.
    16. Gür Ali, Özden & Gürlek, Ragıp, 2020. "Automatic Interpretable Retail forecasting with promotional scenarios," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1389-1406.
    17. Shevelev A.A., 2017. "Bayesian approach to evaluate the impact of external shocks on Russian macroeconomics indicators," World of economics and management / Vestnik NSU. Series: Social and Economics Sciences, Socionet, vol. 17(1), pages 26-40.
    18. Almeida, Caio & Ardison, Kym & Kubudi, Daniela, 2014. "Approximating Risk Premium on a Parametric Arbitrage-free Term Structure Model," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 34(2), November.
    19. Ralf Brüggemann & Christian Kascha, 2017. "Directed Graphs and Variable Selection in Large Vector Autoregressive Models," Working Paper Series of the Department of Economics, University of Konstanz 2017-06, Department of Economics, University of Konstanz.
    20. Carriero, Andrea & Clements, Michael P. & Galvão, Ana Beatriz, 2015. "Forecasting with Bayesian multivariate vintage-based VARs," International Journal of Forecasting, Elsevier, vol. 31(3), pages 757-768.
    21. William Gatt & Germano Ruisi, 2020. "Housing demand shocks, foreign labour inflows and consumption," CBM Working Papers WP/07/2020, Central Bank of Malta.
    22. Prüser Jan & Hanck Christoph, 2021. "A Comparison of Approaches to Select the Informativeness of Priors in BVARs," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 241(4), pages 501-525, August.
    23. И Управления Мир Экономики, 2017. "Байесовский подход к анализу влияния монетарной политики на макроэкономические показатели России. Bayesian approach to the analysis of monetary policy impact on Russian macroeconomics indicators," Мир экономики и управления // Вестник НГУ. Cерия: Cоциально-экономические науки, Socionet;Новосибирский государственный университет, vol. 17(4), pages 53-70.
    24. Dominik Bertsche & Ralf Brüggemann & Christian Kascha, 2023. "Directed graphs and variable selection in large vector autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(2), pages 223-246, March.
    25. Gelper, Sarah & Wilms, Ines & Croux, Christophe, 2016. "Identifying Demand Effects in a Large Network of Product Categories," Journal of Retailing, Elsevier, vol. 92(1), pages 25-39.
    26. Danilo Leiva-Leon, 2017. "Monitoring the Spanish Economy through the Lenses of Structural Bayesian VARs," Occasional Papers 1706, Banco de España.
    27. Caio Almeida & Axel Simonsen & José Valentim Vicente, 2012. "Forecasting Bond Yields with Segmented Term Structure Models," Working Papers Series 288, Central Bank of Brazil, Research Department.
    28. Kwon, Hyuck-Shin & Bang, Doo Won & Kim, Myeong Hyeon, 2017. "Korean Housing Cycle: Implications for Risk Management (Factor-augmented VAR Approach)," KDI Journal of Economic Policy, Korea Development Institute (KDI), vol. 39(3), pages 43-62.
    29. Andrea Carriero & Lorenzo Ricci & Elisabetta Vangelista, 2022. "Expectations and term premia in EFSF bond yields," Working Papers 54, European Stability Mechanism.
    30. Juan Antolin-Diaz & Ivan Petrella & Juan F. Rubio-Ramirez, 2021. "Dividend Momentum and Stock Return Predictability: A Bayesian Approach," FRB Atlanta Working Paper 2021-25, Federal Reserve Bank of Atlanta.
    31. Argyropoulos Efthymios & Tzavalis Elias, 2015. "Term spread regressions of the rational expectations hypothesis of the term structure allowing for risk premium effects," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(1), pages 49-70, February.
    32. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    33. Manfred M. Fischer & Niko Hauzenberger & Florian Huber & Michael Pfarrhofer, 2023. "General Bayesian time‐varying parameter vector autoregressions for modeling government bond yields," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 69-87, January.
    34. Gregory R. Duffee, 2012. "Forecasting interest rates," Economics Working Paper Archive 599, The Johns Hopkins University,Department of Economics.
    35. Doo Won Bang & HyuckShin Kwon, 2022. "Policy Impact Analysis of Housing Policies Using Housing Cycles," SAGE Open, , vol. 12(3), pages 21582440221, July.
    36. Gary Koop & Dimitris Korobilis, 2013. "A new index of financial conditions," Working Papers 1307, University of Strathclyde Business School, Department of Economics.
    37. Caldeira, João F. & Moura, Guilherme V. & Santos, André A.P., 2016. "Predicting the yield curve using forecast combinations," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 79-98.
    38. Oskar Gustafsson & Mattias Villani & Pär Stockhammar, 2023. "Bayesian optimization of hyperparameters from noisy marginal likelihood estimates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 577-595, June.
    39. Dimitrios P. Louzis, 2017. "Macroeconomic and credit forecasts during the Greek crisis using Bayesian VARs," Empirical Economics, Springer, vol. 53(2), pages 569-598, September.
    40. Sebastian Ankargren & Måns Unosson & Yukai Yang, 2018. "A mixed-frequency Bayesian vector autoregression with a steady-state prior," CREATES Research Papers 2018-32, Department of Economics and Business Economics, Aarhus University.
    41. Byrne, Joseph P. & Cao, Shuo & Korobilis, Dimitris, 2017. "Forecasting the term structure of government bond yields in unstable environments," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 209-225.

  57. Demertzis Maria & Marcellino Massimiliano & Viegi Nicola, 2012. "A Credibility Proxy: Tracking US Monetary Developments," The B.E. Journal of Macroeconomics, De Gruyter, vol. 12(1), pages 1-36, June.

    Cited by:

    1. Issler, João Victor & Soares, Ana Flávia, 2019. "Central Bank credibility and inflation expectations: a microfounded forecasting approach," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 812, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    2. Carlos Medel, 2018. "Econometric Analysis on Survey-data-based Anchoring of Inflation Expectations in Chile," Working Papers Central Bank of Chile 825, Central Bank of Chile.
    3. Bems, Rudolfs & Caselli, Francesca & Grigoli, Francesco & Gruss, Bertrand, 2021. "Expectations' Anchoring and Inflation Persistence," CEPR Discussion Papers 16391, C.E.P.R. Discussion Papers.
    4. Mariana Colacelli & Emilio Fernández Corugedo, 2018. "Macroeconomic Effects of Japan’s Demographics: Can Structural Reforms Reverse Them?," IMF Working Papers 2018/248, International Monetary Fund.
    5. Cem Cakmakli & Selva Demiralp, 2020. "A Dynamic Evaluation of Central Bank Credibility," Koç University-TUSIAD Economic Research Forum Working Papers 2015, Koc University-TUSIAD Economic Research Forum.
    6. Alberto Coco & Nicola Viegi, 2020. "The monetary policy of the South African Reserve Bank stance communication and credibility," Working Papers 10024, South African Reserve Bank.
    7. Fernando Nascimento de Oliveira & Wagner Piazza Gaglianone, 2020. "Expectations anchoring indexes for Brazil using Kalman filter: Exploring signals of inflation anchoring in the long term," International Economics, CEPII research center, issue 163, pages 72-91.
    8. End, Nicolas, 2023. "Big Brother is also being watched: Measuring fiscal credibility," Journal of Macroeconomics, Elsevier, vol. 77(C).
    9. Jonas Dovern & Geoff Kenny, 2020. "Anchoring Inflation Expectations in Unconventional Times: Micro Evidence for the Euro Area," International Journal of Central Banking, International Journal of Central Banking, vol. 16(5), pages 309-347, October.
    10. Nicolas End, 2020. "Rousseau's social contract or Machiavelli's virtue? A measure of fiscal credibility," Working Papers halshs-03078704, HAL.
    11. Mayes, David G. & Paloviita, Maritta & Virén, Matti, 2016. "EMU and the Anchoring of Inflation Expectations," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 69(4), pages 341-364.
    12. Bicchal, Motilal, 2022. "Central bank credibility and its effect on stabilization," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 73-94.
    13. Till Strohsal & Rafi Melnick & Dieter Nautz, 2015. "The Time-Varying Degree of Inflation Expectations Anchoring," SFB 649 Discussion Papers SFB649DP2015-028, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    14. Łyziak, Tomasz & Paloviita, Maritta, 2018. "On the formation of inflation expectations in turbulent times: The case of the euro area," Economic Modelling, Elsevier, vol. 72(C), pages 132-139.
    15. Aßhoff, Sina & Belke, Ansgar & Osowski, Thomas, 2021. "Unconventional monetary policy and inflation expectations in the Euro area," Economic Modelling, Elsevier, vol. 102(C).
    16. van der Cruijsen, Carin & Demertzis, Maria, 2011. "How anchored are inflation expectations in EMU countries?," Economic Modelling, Elsevier, vol. 28(1), pages 281-298.
    17. Oinonen, Sami & Paloviita, Maritta & Viren, Matti, 2018. "Effects of monetary policy decisions on professional forecasters' expectations and expectations uncertainty," Bank of Finland Research Discussion Papers 24/2018, Bank of Finland.
    18. Bems, Rudolfs & Caselli, Francesca & Grigoli, Francesco & Gruss, Bertrand, 2020. "Gains from anchoring inflation expectations: Evidence from the taper tantrum shock," Economics Letters, Elsevier, vol. 188(C).
    19. Kristoph Naggert & Robert W. Rich & Joseph Tracy, 2023. "The Anchoring of US Inflation Expectations Since 2012," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2023(11), pages 1-7, July.
    20. Kenny, Geoff & Dovern, Jonas, 2017. "The long-term distribution of expected inflation in the euro area: what has changed since the great recession?," Working Paper Series 1999, European Central Bank.
    21. Dash, Pradyumna & Rohit, Abhishek Kumar & Devaguptapu, Adviti, 2020. "Assessing the (de-)anchoring of households’ long-term inflation expectations in the US," Journal of Macroeconomics, Elsevier, vol. 63(C).
    22. Gießler, Stefan, 2020. "The evolution of monetary policy in Latin American economies: Responsiveness to inflation under different degrees of credibility," IWH Discussion Papers 9/2020, Halle Institute for Economic Research (IWH).

  58. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, 2011. "EUROMIND: a monthly indicator of the euro area economic conditions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(2), pages 439-470, April.

    Cited by:

    1. Michele Modugno & Lucrezia Reichlin & Domenico Giannone & Marta Banbura, 2012. "Nowcasting with Daily Data," 2012 Meeting Papers 555, Society for Economic Dynamics.
    2. Marcellino, Massimiliano & Sivec, Vasja, 2016. "Monetary, fiscal and oil shocks: Evidence based on mixed frequency structural FAVARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 335-348.
    3. Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 366-380, July.
    4. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    5. Libero Monteforte & Valentina Raponi, 2019. "Short‐term forecasts of economic activity: Are fortnightly factors useful?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(3), pages 207-221, April.
    6. Marco Cacciotti & Cecilia Frale & Serena Teobaldo, 2013. "A new methodology for a quarterly measure of the output gap," Working Papers 6, Department of the Treasury, Ministry of the Economy and of Finance.
    7. Reichlin, Lucrezia & Giannone, Domenico & Modugno, Michele & Banbura, Marta, 2012. "Now-casting and the real-time data flow," CEPR Discussion Papers 9112, C.E.P.R. Discussion Papers.
    8. Tommaso Proietti & Alessandra Luati, 2013. "Maximum likelihood estimation of time series models: the Kalman filter and beyond," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 15, pages 334-362, Edward Elgar Publishing.
    9. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    10. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    11. Tommaso Proietti & Martyna Marczak & Gianluigi Mazzi, 2015. "EuroMInd-D: A Density Estimate of Monthly Gross Domestic Product for the Euro Area," CREATES Research Papers 2015-12, Department of Economics and Business Economics, Aarhus University.
    12. Chauvet, Marcelle & Potter, Simon, 2013. "Forecasting Output," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 141-194, Elsevier.
    13. Tommaso Proietti & Alessandro Giovannelli, 2020. "Nowcasting Monthly GDP with Big Data: a Model Averaging Approach," CEIS Research Paper 482, Tor Vergata University, CEIS, revised 12 May 2020.
    14. Bisio, Laura & Moauro, Filippo, 2017. "Temporal disaggregation by dynamic regressions: recent developments in Italian quarterly national accounts," MPRA Paper 80211, University Library of Munich, Germany, revised 14 Jul 2017.
    15. Katja Heinisch & Rolf Scheufele, 2018. "Bottom-up or direct? Forecasting German GDP in a data-rich environment," Empirical Economics, Springer, vol. 54(2), pages 705-745, March.
    16. Raïsa Basselier & David Antonio Liedo & Geert Langenus, 2018. "Nowcasting Real Economic Activity in the Euro Area: Assessing the Impact of Qualitative Surveys," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 14(1), pages 1-46, April.
    17. Donatella Baiardi & Carluccio Bianchi, 2012. "Un Indicatore per la Lombardia e per le Province di Milano e Pavia (Nuova versione)," Quaderni di Dipartimento 158, University of Pavia, Department of Economics and Quantitative Methods.
    18. D’Elia Enrico, 2014. "Predictions vs. Preliminary Sample Estimates: The Case of Eurozone Quarterly GDP," Journal of Official Statistics, Sciendo, vol. 30(3), pages 1-22, September.
    19. Massimiliano Marcellino & Mario Porqueddu & Fabrizio Venditti, 2016. "Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 118-127, January.
    20. Proietti, Tommaso & Giovannelli, Alessandro & Ricchi, Ottavio & Citton, Ambra & Tegami, Christían & Tinti, Cristina, 2021. "Nowcasting GDP and its components in a data-rich environment: The merits of the indirect approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1376-1398.
    21. Ruiz Ortega, Esther & Poncela, Pilar, 2015. "Small versus big-data factor extraction in Dynamic Factor Models: An empirical assessment," DES - Working Papers. Statistics and Econometrics. WS ws1502, Universidad Carlos III de Madrid. Departamento de Estadística.
    22. Ramazan Yanik & Asfia Binte Osman & Ozcan Ozturk, 2020. "Impact of manufacturing PMI on stock market index: A study on Turkey," Journal of Administrative and Business Studies, Professor Dr. Usman Raja, vol. 6(3), pages 104-108.
    23. Dr. Gregor Bäurle & Elizabeth Steiner & Dr. Gabriel Züllig, 2018. "Forecasting the production side of GDP," Working Papers 2018-16, Swiss National Bank.
    24. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2020. "Regional output growth in the United Kingdom: More timely and higher frequency estimates from 1970," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(2), pages 176-197, March.
    25. Barhoumi, K. & Darné, O. & Ferrara, L., 2013. "Dynamic Factor Models: A review of the Literature ," Working papers 430, Banque de France.
    26. Cecilia Frale & Stefano Grassi & Massimiliano Marcellino & Gianluigi Mazzi & Tommaso Proietti, 2013. "EuroMInd-C: a Disaggregate Monthly Indicator of Economic Activity for the Euro Area and member countries," CEIS Research Paper 287, Tor Vergata University, CEIS, revised 01 Oct 2013.
    27. Cecilia Frale, Serena Teobaldo, Marco Cacciotti, Alessandra Caretta, 2013. "A Quarterly Measure Of Potential Output In The New European Fiscal Framework," RIEDS - Rivista Italiana di Economia, Demografia e Statistica - The Italian Journal of Economic, Demographic and Statistical Studies, SIEDS Societa' Italiana di Economia Demografia e Statistica, vol. 67(2), pages 181-197, April-Jun.
    28. Martyna Marczak & Víctor Gómez, 2017. "Monthly US business cycle indicators: a new multivariate approach based on a band-pass filter," Empirical Economics, Springer, vol. 52(4), pages 1379-1408, June.
    29. Donato Ceci & Andrea Silvestrini, 2023. "Nowcasting the state of the Italian economy: The role of financial markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1569-1593, November.
    30. Kajal Lahiri & George Monokroussos, 2011. "Nowcasting US GDP: The role of ISM Business Surveys," Discussion Papers 11-01, University at Albany, SUNY, Department of Economics.
    31. Pinkwart, Nicolas, 2018. "Short-term forecasting economic activity in Germany: A supply and demand side system of bridge equations," Discussion Papers 36/2018, Deutsche Bundesbank.
    32. Mokinski, Frieder, 2016. "Using time-stamped survey responses to measure expectations at a daily frequency," International Journal of Forecasting, Elsevier, vol. 32(2), pages 271-282.
    33. Cobb, Marcus P A, 2018. "Improving Underlying Scenarios for Aggregate Forecasts: A Multi-level Combination Approach," MPRA Paper 88593, University Library of Munich, Germany.
    34. Foroni, Claudia & Marcellino, Massimiliano, 2014. "A comparison of mixed frequency approaches for nowcasting Euro area macroeconomic aggregates," International Journal of Forecasting, Elsevier, vol. 30(3), pages 554-568.
    35. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2013. "On the Stratonovich – Kalman - Bucy filtering algorithm application for accurate characterization of financial time series with use of state-space model by central banks," MPRA Paper 50235, University Library of Munich, Germany.
    36. Raffaele Mattera & Michelangelo Misuraca & Maria Spano & Germana Scepi, 2023. "Mixed frequency composite indicators for measuring public sentiment in the EU," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2357-2382, June.
    37. Moauro, Filippo, 2010. "A monthly indicator of employment in the euro area: real time analysis of indirect estimates," MPRA Paper 27797, University Library of Munich, Germany, revised 30 Dec 2010.
    38. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    39. Alessandro Giovannelli & Marco Lippi & Tommaso Proietti, 2023. "Band-Pass Filtering with High-Dimensional Time Series," CEIS Research Paper 559, Tor Vergata University, CEIS, revised 15 Jun 2023.
    40. Juan Pablo Cote-Barón & Karen L. Pulido-Mahecha & Nicol Valeria Rodríguez-Rodríguez & Carlos D. Rojas-Martínez, 2023. "El ISAE: Un Indicador para Monitorear la Actividad Económica Colombiana en Alta Frecuencia," Borradores de Economia 1225, Banco de la Republica de Colombia.
    41. Gary Koop & Stuart McIntyre & James Mitchell, 2020. "UK regional nowcasting using a mixed frequency vector auto‐regressive model with entropic tilting," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 91-119, January.

  59. Deák, Szabolcs & Fontagné, Lionel & Maffezzoli, Marco & Marcellino, Massimiliano, 2011. "LSM: A DSGE model for Luxembourg," Economic Modelling, Elsevier, vol. 28(6), pages 2862-2872.
    See citations under working paper version above.
  60. Marcellino, Massimiliano & Musso, Alberto, 2011. "The reliability of real-time estimates of the euro area output gap," Economic Modelling, Elsevier, vol. 28(4), pages 1842-1856, July.
    See citations under working paper version above.
  61. Andrea Carriero & Massimiliano Marcellino, 2011. "Sectoral Survey‐based Confidence Indicators for Europe," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(2), pages 175-206, April.
    See citations under working paper version above.
  62. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2011. "Forecasting large datasets with Bayesian reduced rank multivariate models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 735-761, August.
    See citations under working paper version above.
  63. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2011. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area," International Journal of Forecasting, Elsevier, vol. 27(2), pages 529-542, April.
    See citations under working paper version above.
  64. Angelini, Elena & Marcellino, Massimiliano, 2011. "Econometric analyses with backdated data: Unified Germany and the euro area," Economic Modelling, Elsevier, vol. 28(3), pages 1405-1414, May.
    See citations under working paper version above.
  65. Òscar Jordà & Massimiliano Marcellino, 2010. "Path forecast evaluation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 635-662.
    See citations under working paper version above.
  66. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, 2010. "Survey data as coincident or leading indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 109-131.
    See citations under working paper version above.
  67. Kapetanios, George & Marcellino, Massimiliano, 2010. "Cross-sectional averaging and instrumental variable estimation with many weak instruments," Economics Letters, Elsevier, vol. 108(1), pages 36-39, July.
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  68. Massimiliano Marcellino & Christian Schumacher, 2010. "Factor MIDAS for Nowcasting and Forecasting with Ragged‐Edge Data: A Model Comparison for German GDP," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(4), pages 518-550, August.

    Cited by:

    1. Claudio, João C. & Heinisch, Katja & Holtemöller, Oliver, 2019. "Nowcasting East German GDP growth: A MIDAS approach," IWH Discussion Papers 24/2019, Halle Institute for Economic Research (IWH).
    2. Qian, Hang, 2012. "Essays on statistical inference with imperfectly observed data," ISU General Staff Papers 201201010800003618, Iowa State University, Department of Economics.
    3. Christiane Baumeister & Pierre Guérin, 2020. "A Comparison of Monthly Global Indicators for Forecasting Growth," CESifo Working Paper Series 8656, CESifo.
    4. Michele Modugno & Lucrezia Reichlin & Domenico Giannone & Marta Banbura, 2012. "Nowcasting with Daily Data," 2012 Meeting Papers 555, Society for Economic Dynamics.
    5. Caroline Jardet & Baptiste Meunier, 2022. "Nowcasting world GDP growth with high‐frequency data," Post-Print hal-03647097, HAL.
    6. Marcellino, Massimiliano & Sivec, Vasja, 2016. "Monetary, fiscal and oil shocks: Evidence based on mixed frequency structural FAVARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 335-348.
    7. Ligia Alba Melo-Becerra & Jorge Enrique Ramos-Forero & Ligia Marcela Parrado-Galvis & Hector Manuel Zarate-Solano, 2016. "Bonanzas y crisis de la actividad petrolera y su efecto sobre la economía colombiana," Borradores de Economia 961, Banco de la Republica de Colombia.
    8. Hager Ben Romdhane, 2021. "Nowcasting in Tunisia using large datasets and mixed frequency models," IHEID Working Papers 11-2021, Economics Section, The Graduate Institute of International Studies.
    9. Luci Alessi & Eric Ghysels & Luca Onorante & Richard Peach & Simon M. Potter, 2014. "Central bank macroeconomic forecasting during the global financial crisis: the European Central Bank and Federal Reserve Bank of New York experiences," Staff Reports 680, Federal Reserve Bank of New York.
    10. Cecilia Frale & Libero Monteforte, "undated". "FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure," Working Papers 3, Department of the Treasury, Ministry of the Economy and of Finance.
    11. Hanan Naser, 2015. "Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods," Empirical Economics, Springer, vol. 49(2), pages 449-479, September.
    12. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Real-time nowcasting with a Bayesian mixed frequency model with stochastic volatility," Working Papers (Old Series) 1227, Federal Reserve Bank of Cleveland.
    13. Paolo Andreini & Cosimo Izzo & Giovanni Ricco, 2020. "Deep Dynamic Factor Models," Papers 2007.11887, arXiv.org, revised May 2023.
    14. Jennifer L. Castle & Michael P. Clements & David F. Hendry, 2016. "An Overview of Forecasting Facing Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 3-23, September.
    15. Cláudia Duarte, 2015. "Covariate-augmented unit root tests with mixed-frequency data," Working Papers w201507, Banco de Portugal, Economics and Research Department.
    16. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
    17. Bouwman, Kees E. & Jacobs, Jan P.A.M., 2005. "Forecasting with real-time macroeconomic data: the ragged-edge problem and revisions," CCSO Working Papers 200505, University of Groningen, CCSO Centre for Economic Research.
    18. Reichlin, Lucrezia & Giannone, Domenico & Modugno, Michele & Banbura, Marta, 2012. "Now-casting and the real-time data flow," CEPR Discussion Papers 9112, C.E.P.R. Discussion Papers.
    19. Schwarzmüller, Tim, 2015. "Model pooling and changes in the informational content of predictors: An empirical investigation for the euro area," Kiel Working Papers 1982, Kiel Institute for the World Economy (IfW Kiel).
    20. Matteo Mogliani & Anna Simoni, 2020. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Post-Print hal-03089878, HAL.
    21. Monokroussos, George & Zhao, Yongchen, 2020. "Nowcasting in real time using popularity priors," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1173-1180.
    22. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Working Paper 2013/06, Norges Bank.
    23. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2014. "Combining distributions of real-time forecasts: An application to U.S. growth," Research Memorandum 027, Maastricht University, Graduate School of Business and Economics (GSBE).
    24. Valadkhani, Abbas & Smyth, Russell, 2017. "How do daily changes in oil prices affect US monthly industrial output?," Energy Economics, Elsevier, vol. 67(C), pages 83-90.
    25. Laurent Ferrara & Clément Marsilli & Juan-Pablo Ortega, 2013. "Forecasting US growth during the Great Recession: Is the financial volatility the missing ingredient?," Working Papers hal-04141198, HAL.
    26. Sarun Kamolthip, 2021. "Macroeconomic forecasting with LSTM and mixed frequency time series data," Papers 2109.13777, arXiv.org.
    27. Marie Bessec & Othman Bouabdallah, 2015. "Forecasting GDP over the Business Cycle in a Multi-Frequency and Data-Rich Environment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(3), pages 360-384, June.
    28. Stankevich, Ivan, 2020. "Comparison of macroeconomic indicators nowcasting methods: Russian GDP case," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 59, pages 113-127.
    29. Duarte, Cláudia & Rodrigues, Paulo M.M. & Rua, António, 2017. "A mixed frequency approach to the forecasting of private consumption with ATM/POS data," International Journal of Forecasting, Elsevier, vol. 33(1), pages 61-75.
    30. Luciani, Matteo & Pundit, Madhavi & Ramayandi, Arief & Veronese , Giovanni, 2015. "Nowcasting Indonesia," ADB Economics Working Paper Series 471, Asian Development Bank.
    31. Claudia FORONI & Massimiliano MARCELLINO, 2012. "A Comparison of Mixed Frequency Approaches for Modelling Euro Area Macroeconomic Variables," Economics Working Papers ECO2012/07, European University Institute.
    32. Tóth, Peter, 2014. "Malý dynamický faktorový model na krátkodobé prognózovanie slovenského HDP [A Small Dynamic Factor Model for the Short-Term Forecasting of Slovak GDP]," MPRA Paper 63713, University Library of Munich, Germany.
    33. David Iselin & Boriss Siliverstovs, 2013. "Using Newspapers for Tracking the Business Cycle," KOF Working papers 13-337, KOF Swiss Economic Institute, ETH Zurich.
    34. Laurent Ferrara & Clément Marsilli, 2019. "Nowcasting global economic growth: A factor‐augmented mixed‐frequency approach," The World Economy, Wiley Blackwell, vol. 42(3), pages 846-875, March.
    35. Christian Glocker & Philipp Wegmüller, 2017. "Business Cycle Dating and Forecasting with Real-time Swiss GDP Data," WIFO Working Papers 542, WIFO.
    36. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    37. Giovanni Ballarin & Petros Dellaportas & Lyudmila Grigoryeva & Marcel Hirt & Sophie van Huellen & Juan-Pablo Ortega, 2022. "Reservoir Computing for Macroeconomic Forecasting with Mixed Frequency Data," Papers 2211.00363, arXiv.org, revised Jan 2024.
    38. Jonas E. Arias & Minchul Shin, 2020. "Tracking U.S. Real GDP Growth During the Pandemic," Economic Insights, Federal Reserve Bank of Philadelphia, vol. 5(3), pages 9-14, September.
    39. Guillaume Bagnarosa & Mark Cummins & Michael Dowling & Fearghal Kearney, 2022. "Commodity risk in European dairy firms," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 49(1), pages 151-181.
    40. Kapetanios, George & Marcellino, Massimiliano & Papailias, Fotis, 2016. "Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 369-382.
    41. Guido Bulligan & Massimiliano Marcellino & Fabrizio Venditti, 2012. "Forecasting economic activity with higher frequency targeted predictors," Temi di discussione (Economic working papers) 847, Bank of Italy, Economic Research and International Relations Area.
    42. Banerjee, Anindya & Marcellino, Massimiliano, 2008. "Factor-augmented Error Correction Models," CEPR Discussion Papers 6707, C.E.P.R. Discussion Papers.
    43. Danilo Cascaldi-Garcia & Thiago Revil T. Ferreira & Domenico Giannone & Michele Modugno, 2021. "Back to the Present: Learning about the Euro Area through a Now-casting Model," International Finance Discussion Papers 1313, Board of Governors of the Federal Reserve System (U.S.).
    44. Foroni, Claudia & Marcellino, Massimiliano & Schumacher, Christian, 2011. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," Discussion Paper Series 1: Economic Studies 2011,35, Deutsche Bundesbank.
    45. Matteo Luciani & Lorenzo Ricci, 2014. "Nowcasting Norway," International Journal of Central Banking, International Journal of Central Banking, vol. 10(4), pages 215-248, December.
    46. Alexander Chudik & Valerie Grossman & M. Hashem Pesaran, 2014. "A multi-country approach to forecasting output growth using PMIs," Globalization Institute Working Papers 213, Federal Reserve Bank of Dallas.
    47. Raul Ibarra & Luis M. Gomez-Zamudio, 2017. "Are Daily Financial Data Useful for Forecasting GDP? Evidence from Mexico," Economía Journal, The Latin American and Caribbean Economic Association - LACEA, vol. 0(Spring 20), pages 173-203, April.
    48. Daniel Kaufmann & Rolf Scheufele, 2015. "Business tendency surveys and macroeconomic fluctuations," KOF Working papers 15-378, KOF Swiss Economic Institute, ETH Zurich.
    49. Katja Heinisch & Rolf Scheufele, 2018. "Bottom-up or direct? Forecasting German GDP in a data-rich environment," Empirical Economics, Springer, vol. 54(2), pages 705-745, March.
    50. Soybilgen, Barış & Yazgan, Ege, 2018. "Evaluating nowcasts of bridge equations with advanced combination schemes for the Turkish unemployment rate," Economic Modelling, Elsevier, vol. 72(C), pages 99-108.
    51. Elena Andreou & Patrick Gagliardini & Eric Ghysels & Mirco Rubin, 2016. "Is Industrial Production Still the Dominant Factor for the US Economy?," Swiss Finance Institute Research Paper Series 16-11, Swiss Finance Institute.
    52. Christian Glocker & Serguei Kaniovski, 2022. "Macroeconometric forecasting using a cluster of dynamic factor models," Empirical Economics, Springer, vol. 63(1), pages 43-91, July.
    53. Morita, Hiroshi, 2022. "Forecasting GDP growth using stock returns in Japan: A factor-augmented MIDAS approach," Discussion paper series HIAS-E-118, Hitotsubashi Institute for Advanced Study, Hitotsubashi University.
    54. Iyer , Tara & Sen Gupta, Abhijit, 2019. "Nowcasting Economic Growth in India: The Role of Rainfall," ADB Economics Working Paper Series 593, Asian Development Bank.
    55. Hanslin Grossmann, Sandra & Scheufele, Rolf, 2015. "Foreign PMIs: A reliable indicator for Swiss exports," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112830, Verein für Socialpolitik / German Economic Association.
    56. Catherine Doz & Peter Fuleky, 2019. "Dynamic Factor Models," Working Papers 2019-4, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    57. Daniel Hopp, 2022. "Benchmarking Econometric and Machine Learning Methodologies in Nowcasting," Papers 2205.03318, arXiv.org.
    58. Thomas Dimpfl & Tobias Langen, 2019. "How Unemployment Affects Bond Prices: A Mixed Frequency Google Nowcasting Approach," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 551-573, August.
    59. Gorgi, Paolo & Koopman, Siem Jan & Li, Mengheng, 2019. "Forecasting economic time series using score-driven dynamic models with mixed-data sampling," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1735-1747.
    60. Thomas B. Götz & Alain W. Hecq, 2019. "Granger Causality Testing in Mixed‐Frequency VARs with Possibly (Co)Integrated Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 40(6), pages 914-935, November.
    61. Dennis Kant & Andreas Pick & Jasper de Winter, 2022. "Nowcasting GDP using machine learning methods," Working Papers 754, DNB.
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    37. Arpita Chatterjee, 2014. "Globalization and Monetary Policy Comovement: Evidence from G-7 Countries," Discussion Papers 2014-19, School of Economics, The University of New South Wales.
    38. Eder, Andreas & Koller, Wolfgang & Mahlberg, Bernhard, 2019. "Price Competitiveness in the European Monetary Union: A Decomposition of Inflation Differentials based on the Leontief Input-Output Price Model for the Period 2000 to 2014," MPRA Paper 95158, University Library of Munich, Germany.
    39. Paweł Gajewski, 2017. "Sources of Regional Inflation in Poland," Eastern European Economics, Taylor & Francis Journals, vol. 55(3), pages 261-276, May.
    40. Harry Aginta, 2022. "Spatiotemporal analysis of regional inflation in an emerging country: The case of Indonesia," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(3), pages 667-688, June.
    41. Harry Aginta, 2021. "Spatial dynamics of consumer price in Indonesia: convergence clubs and conditioning factors," Asia-Pacific Journal of Regional Science, Springer, vol. 5(2), pages 427-451, June.
    42. Christina Bräuning & Ralf Fendel, 2018. "National information and euro area monetary policy: a generalized ordered choice approach," Empirical Economics, Springer, vol. 54(2), pages 501-522, March.
    43. Shu-hen Chiang, 2016. "Rising residential rents in Chinese mega cities: The role of monetary policy," Urban Studies, Urban Studies Journal Limited, vol. 53(16), pages 3493-3509, December.
    44. Ivan F Dumka, 2016. "Coordinated wage setting and social partnership under EMU. A framework for analysis and results from Belgium, Germany and the Netherlands," Transfer: European Review of Labour and Research, , vol. 22(4), pages 445-460, November.
    45. Alyona Nelyubina, 2021. "Forecasting Regional Indicators Based on the Quarterly Projection Model," Russian Journal of Money and Finance, Bank of Russia, vol. 80(2), pages 50-75, June.
    46. Gent Bajraj & Guillermo Carlomagno & Juan M. Wlasiuk, 2023. "Where is the Inflation? The Diverging Patterns of Prices of Goods and Services," Working Papers Central Bank of Chile 969, Central Bank of Chile.
    47. Quint, Dominic, 2014. "Is it really more dispersed? Measuring and comparing the stress from the common monetary policy in the euro area," Discussion Papers 2014/13, Free University Berlin, School of Business & Economics.
    48. International Monetary Fund, 2011. "Spain: Selected Issues," IMF Staff Country Reports 2011/216, International Monetary Fund.
    49. Jing Zeng, 2016. "Combining country-specific forecasts when forecasting Euro area macroeconomic aggregates," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 43(2), pages 415-444, May.

  73. Andreas Beyer & Roger E. A. Farmer & Jérôme Henry & Massimiliano Marcellino, 2008. "Factor analysis in a model with rational expectations," Econometrics Journal, Royal Economic Society, vol. 11(2), pages 271-286, July.
    See citations under working paper version above.
  74. David F. Hendry & Massimiliano Marcellino & Grayham E. Mizon, 2008. "Guest Editors’ Introduction to Special Issue on Encompassing," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 715-719, December.

    Cited by:

    1. David Hendry & Grayham E. Mizon, 2016. "Improving the Teaching of Econometrics," Economics Series Working Papers 785, University of Oxford, Department of Economics.
    2. D. R. Cox, 2013. "A return to an old paper: ‘Tests of separate families of hypotheses’," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(2), pages 207-215, March.

  75. Massimiliano Marcellino & Barbara Rossi, 2008. "Model Selection for Nested and Overlapping Nonlinear, Dynamic and Possibly Mis‐specified Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 867-893, December.

    Cited by:

    1. Gospodinov, Nikolay & Kan, Raymond & Robotti, Cesare, 2013. "Chi-squared tests for evaluation and comparison of asset pricing models," Journal of Econometrics, Elsevier, vol. 173(1), pages 108-125.
    2. Mayer, Walter J. & Liu, Feng & Dang, Xin, 2017. "Improving the power of the Diebold–Mariano–West test for least squares predictions," International Journal of Forecasting, Elsevier, vol. 33(3), pages 618-626.
    3. Christophe Bontemps & Grayham E. Mizon, 2008. "Encompassing: Concepts and Implementation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(s1), pages 721-750, December.
    4. Lavergne, Pascal & Bertail, Patrice, 2020. "Bootstrapping Quasi Likelihood Ratio Tests under Misspecification," TSE Working Papers 20-1102, Toulouse School of Economics (TSE).
    5. Bu Ruijun & Cheng Jie & Hadri Kaddour, 2017. "Specification analysis in regime-switching continuous-time diffusion models for market volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(1), pages 65-80, February.
    6. Francesco Battaglia & Mattheos Protopapas, 2012. "An analysis of global warming in the Alpine region based on nonlinear nonstationary time series models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(3), pages 315-334, August.

  76. Ralf Brüggemann & Helmut Lütkepohl & Massimiliano Marcellino, 2008. "Forecasting euro area variables with German pre-EMU data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(6), pages 465-481.
    See citations under working paper version above.
  77. Massimiliano Marcellino, 2008. "A linear benchmark for forecasting GDP growth and inflation?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 305-340.

    Cited by:

    1. Lauren Hackler & Frank Hefner & Mark D. Witte, 2020. "The Effects of IMF Loan Condition Compliance on GDP Growth," The American Economist, Sage Publications, vol. 65(1), pages 88-96, March.
    2. Arabinda Basistha, 2023. "Estimation of short‐run predictive factor for US growth using state employment data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 34-50, January.
    3. Tesi Aliaj & Milos Ciganovic & Massimiliano Tancioni, 2023. "Nowcasting inflation with Lasso‐regularized vector autoregressions and mixed frequency data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 464-480, April.
    4. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
    5. Cobb, Marcus P A, 2017. "Joint Forecast Combination of Macroeconomic Aggregates and Their Components," MPRA Paper 76556, University Library of Munich, Germany.
    6. Jason Furman, 2022. "Why Did (Almost) No One See the Inflation Coming?," Intereconomics: Review of European Economic Policy, Springer;ZBW - Leibniz Information Centre for Economics;Centre for European Policy Studies (CEPS), vol. 57(2), pages 79-86, March.
    7. Salisu, Afees A. & Ogbonna, Ahamuefula E., 2019. "Another look at the energy-growth nexus: New insights from MIDAS regressions," Energy, Elsevier, vol. 174(C), pages 69-84.
    8. Milena Lipovina-Božović, 2013. "A Comparison Of The Var Model And The Pc Factor Model In Forecasting Inflation In Montenegro," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 58(198), pages 115-136, July - Se.
    9. Carstensen, Kai & Wohlrabe, Klaus & Ziegler, Christina, 2011. "Predictive ability of business cycle indicators under test: A case study for the Euro area industrial production," Munich Reprints in Economics 19953, University of Munich, Department of Economics.
    10. Anh Dinh Minh Nguyen, 2017. "U.K. Monetary Policy under Inflation Targeting," Bank of Lithuania Working Paper Series 41, Bank of Lithuania.
    11. Akdoğan, Kurmaş, 2020. "Fundamentals versus speculation in oil market: The role of asymmetries in price adjustment?," Resources Policy, Elsevier, vol. 67(C).
    12. Cobb, Marcus P A, 2017. "Forecasting Economic Aggregates Using Dynamic Component Grouping," MPRA Paper 81585, University Library of Munich, Germany.
    13. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
    14. Arabinda Basistha & Richard Startz, 2023. "Measuring Persistent Global Economic Factors with Output, Commodity Price, and Commodity Currency Data," Working Papers 23-05, Department of Economics, West Virginia University.
    15. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Working Papers 22-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management.
    16. Basistha, Arabinda & Kurov, Alexander & Wolfe, Marketa Halova, 2019. "Volatility Forecasting: The Role of Internet Search Activity and Implied Volatility," MPRA Paper 111037, University Library of Munich, Germany.
    17. Bahar Şen Doğan & Murat Midiliç, 2019. "Forecasting Turkish real GDP growth in a data-rich environment," Empirical Economics, Springer, vol. 56(1), pages 367-395, January.
    18. Cobb, Marcus P A, 2018. "Improving Underlying Scenarios for Aggregate Forecasts: A Multi-level Combination Approach," MPRA Paper 88593, University Library of Munich, Germany.
    19. Khan, Md. Tareq Ferdous & Kundu, Nobinkhor, 2012. "Future Contribution of Export and Import to GDP in Bangladesh: A Box-Jenkins Approach," MPRA Paper 65153, University Library of Munich, Germany, revised 15 Jun 2012.
    20. Kurmaş Akdoğan, 2019. "Size and sign asymmetries in house price adjustments," Applied Economics, Taylor & Francis Journals, vol. 51(48), pages 5268-5281, October.
    21. Blerina Vika & Ilir Vika, 2021. "Forecasting Albanian Time Series with Linear and Nonlinear Univariate Models," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 10, September.
    22. Dimitrios P. Louzis, 2017. "Macroeconomic and credit forecasts during the Greek crisis using Bayesian VARs," Empirical Economics, Springer, vol. 53(2), pages 569-598, September.
    23. Anesti, Nikoleta & Kalamara, Eleni & Kapetanios, George, 2021. "Forecasting UK GDP growth with large survey panels," Bank of England working papers 923, Bank of England.
    24. Christian Kascha & Francesco Ravazzolo, 2008. "Combining inflation density forecasts," Working Paper 2008/22, Norges Bank.
    25. Philippe Goulet Coulombe, 2022. "A Neural Phillips Curve and a Deep Output Gap," Papers 2202.04146, arXiv.org.
    26. David N. DeJong & Hariharan Dharmarajan & Roman Liesenfeld & Jean-Francois Richard, 2008. "Exploiting Non-Linearities in GDP Growth for Forecasting and Anticipating Regime Changes," Working Paper 367, Department of Economics, University of Pittsburgh, revised Sep 2008.
    27. Byron Botha & Tim Olds & Geordie Reid & Daan Steenkamp & Rossouw van Jaarsveld, 2021. "Nowcasting South African gross domestic product using a suite of statistical models," South African Journal of Economics, Economic Society of South Africa, vol. 89(4), pages 526-554, December.

  78. Dreger, Christian & Marcellino, Massimiliano, 2007. "A macroeconometric model for the Euro economy," Journal of Policy Modeling, Elsevier, vol. 29(1), pages 1-13.

    Cited by:

    1. Albacete, Rebeca & Espasa, Antoni, 2005. "Forecasting inflation in the euro area using monthly time series models and quarterly econometric models," DES - Working Papers. Statistics and Econometrics. WS ws050401, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Klein, Lawrence R. & Kushnirsky, Fyodor I. & Maksymenko, Svitlana V., 2012. "Macroeconometric study of Ukraine's growth and reform," Journal of Policy Modeling, Elsevier, vol. 34(3), pages 325-340.
    3. Akbar, Muhammad & Ahmad, Eatzaz, 2021. "Repercussions of exchange rate depreciation on the economy of Pakistan: Simulation analysis using macroeconometric model," Journal of Policy Modeling, Elsevier, vol. 43(3), pages 574-600.
    4. Scheufele, Rolf, 2008. "Das makroökonometrische Modell des IWH: Eine angebotsseitige Betrachtung," IWH Discussion Papers 9/2008, Halle Institute for Economic Research (IWH).
    5. Christian Dreger & Hans-Eggert Reimers, 2012. "Does Euro Area Membership Affect the Relation between GDP Growth and Public Debt?," Discussion Papers of DIW Berlin 1249, DIW Berlin, German Institute for Economic Research.
    6. Fakhri J. Hasanov & Noha Razek, 2023. "Oil and Non-Oil Determinants of Saudi Arabia’s International Competitiveness: Historical Analysis and Policy Simulations," Sustainability, MDPI, vol. 15(11), pages 1-39, June.
    7. Jérôme Creel & Bruno Ducoudré & Catherine Mathieu & Henri Sterdyniak, 2005. "Doit-on oublier la politique budgétaire ?. Une analyse critique de la nouvelle théorie anti-keynésienne des finances publiques," Revue de l'OFCE, Presses de Sciences-Po, vol. 92(1), pages 43-97.
    8. Pierre-Olivier Beffy & Xavier Bonnet & Brieuc Monfort & Matthieu Darracq-Pariès & Jérôme Henry, 2003. "MZE, un modèle macroéconométrique pour la zone euro ; suivi d'un commentaire de Jérome Henry," Économie et Statistique, Programme National Persée, vol. 367(1), pages 3-37.
    9. Christian Dreger & Yanqun Zhang, 2013. "Does the economic integration of China affect growth and inflation in industrial countries?," FIW Working Paper series 116, FIW.
    10. Christian Dreger & Florian Zinsmeister, 2007. "Das IMM: ein makroökonometrisches Mehrländermodell," Vierteljahrshefte zur Wirtschaftsforschung / Quarterly Journal of Economic Research, DIW Berlin, German Institute for Economic Research, vol. 76(4), pages 35-46.
    11. Paredes, Joan & Pedregal, Diego J. & Pérez, Javier J., 2014. "Fiscal policy analysis in the euro area: Expanding the toolkit," Journal of Policy Modeling, Elsevier, vol. 36(5), pages 800-823.
    12. Alberto Bagnai & Christian Alexander Mongeau Ospina, 2014. "The a/simmetrie annual macroeconometric model of the Italian economy: structure and properties," a/ Working Papers Series 1405, Italian Association for the Study of Economic Asymmetries, Rome (Italy).
    13. Antipa, Pamfili & Barhoumi, Karim & Brunhes-Lesage, Véronique & Darné, Olivier, 2012. "Nowcasting German GDP: A comparison of bridge and factor models," Journal of Policy Modeling, Elsevier, vol. 34(6), pages 864-878.
    14. Qiao, Zhuo & Chu, Patrick Kuok-Kun, 2014. "Does fine wine price contain useful information to forecast GDP? Evidence from major developed countries," Economic Modelling, Elsevier, vol. 38(C), pages 75-79.
    15. Aizhan Bolatbayeva & Alisher Tolepbergen & Nurdaulet Abilov, 2020. "A macroeconometric model for Russia," Russian Journal of Economics, ARPHA Platform, vol. 6(2), pages 114-143, June.
    16. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2005. "Leading Indicators for Euro‐area Inflation and GDP Growth," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 785-813, December.
    17. Barabas, György & Gebhardt, Heinz & Münch, Heinz Josef & Schmidt, Christoph M. & Schmidt, Torsten & Breitung, Jörg, 2005. "Methoden mittelfristiger gesamtwirtschaftlicher Projektionen: Dienstleistungsvorhaben im Auftrag des Bundesministeriums für Wirtschaft und Arbeit, Projektnummer 02/05. Vorläufiger Endbericht," RWI Projektberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, number 69948.
    18. Espasa, Antoni & Senra, Eva, 2017. "22 Years of inflation assessment and forecasting experience at the bulletin of EU & US inflation and macroeconomic analysis," DES - Working Papers. Statistics and Econometrics. WS 24678, Universidad Carlos III de Madrid. Departamento de Estadística.
    19. Olofin, S.O. & Olubusoye, O.E. & Mordi, C.N.O. & Salisu, A.A. & Adeleke, A.I. & Orekoya, S.O. & Olowookere, A.E. & Adebiyi, M.A., 2014. "A small macroeconometric model of the Nigerian economy," Economic Modelling, Elsevier, vol. 39(C), pages 305-313.
    20. Olofin, S.O. & Salisu, A.A & Tule, M.K, 2020. "Revised Small Macro-Econometric Model Of The Nigerian Economy," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 20(1), pages 97-116.
    21. Villaverde, José & Maza, Adolfo, 2009. "The robustness of Okun's law in Spain, 1980-2004: Regional evidence," Journal of Policy Modeling, Elsevier, vol. 31(2), pages 289-297.
    22. Salami, Habibollah & Shahnooshi, Naser & Thomson, Kenneth J., 2009. "The economic impacts of drought on the economy of Iran: An integration of linear programming and macroeconometric modelling approaches," Ecological Economics, Elsevier, vol. 68(4), pages 1032-1039, February.

  79. Carriero, Andrea & Marcellino, Massimiliano, 2007. "A comparison of methods for the construction of composite coincident and leading indexes for the UK," International Journal of Forecasting, Elsevier, vol. 23(2), pages 219-236.
    See citations under working paper version above.
  80. Ana Beatriz Galvão & Michael Artis & Massimiliano Marcellino, 2007. "The transmission mechanism in a changing world," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 39-61.
    See citations under working paper version above.
  81. Massimiliano Marcellino, 2007. "Pooling‐Based Data Interpolation and Backdating," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(1), pages 53-71, January.
    See citations under working paper version above.
  82. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
    See citations under working paper version above.
  83. Corielli, Francesco & Marcellino, Massimiliano, 2006. "Factor based index tracking," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2215-2233, August.
    See citations under working paper version above.
  84. Angelini, Elena & Henry, Jerome & Marcellino, Massimiliano, 2006. "Interpolation and backdating with a large information set," Journal of Economic Dynamics and Control, Elsevier, vol. 30(12), pages 2693-2724, December.
    See citations under working paper version above.
  85. Banerjee, Anindya & Marcellino, Massimiliano, 2006. "Are there any reliable leading indicators for US inflation and GDP growth?," International Journal of Forecasting, Elsevier, vol. 22(1), pages 137-151.
    See citations under working paper version above.
  86. Marcellino, Massimiliano, 2006. "Some stylized facts on non-systematic fiscal policy in the Euro area," Journal of Macroeconomics, Elsevier, vol. 28(3), pages 461-479, September.
    See citations under working paper version above.
  87. Carlo A. Favero & Massimiliano Marcellino, 2005. "Modelling and Forecasting Fiscal Variables for the Euro Area," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 755-783, December.
    See citations under working paper version above.
  88. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2005. "Leading Indicators for Euro‐area Inflation and GDP Growth," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 785-813, December.
    See citations under working paper version above.
  89. Massimiliano Marcellino & Carlo A. Favero & Francesca Neglia, 2005. "Principal components at work: the empirical analysis of monetary policy with large data sets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(5), pages 603-620.
    See citations under working paper version above.
  90. Anindya Banerjee & Massimiliano Marcellino & Chiara Osbat, 2005. "Testing for PPP: Should we use panel methods?," Empirical Economics, Springer, vol. 30(1), pages 77-91, January.
    See citations under working paper version above.
  91. Michael Artis & Massimiliano Marcellino & Tommaso Proietti, 2005. "Business Cycles in the New EU Member Countries and their Conformity with the Euro Area," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(1), pages 7-41.

    Cited by:

    1. Oscar Bajo-Rubio & Carmen Díaz-Roldán, 2005. "Characterizing macroeconomic shocks in the CEECs," Economic Change and Restructuring, Springer, vol. 38(3), pages 227-234, December.
    2. Artis, Michael, 2006. "What Do We Now Know About Currency Unions?," CEPR Discussion Papers 5677, C.E.P.R. Discussion Papers.
    3. Sandra Eickmeier & Joerg Breitung, 2006. "Business cycle transmission from the euro area to CEECs," Computing in Economics and Finance 2006 229, Society for Computational Economics.
    4. Eickmeier, Sandra & Breitung, Jorg, 2006. "How synchronized are new EU member states with the euro area? Evidence from a structural factor model," Journal of Comparative Economics, Elsevier, vol. 34(3), pages 538-563, September.
    5. Gächter, Simon & Riedl, Alesandra & Ritzberger-Grünwald, Doris, 2013. "Business cycle convergence or decoupling? Economic adjustment in CESEE during the crisis," BOFIT Discussion Papers 3/2013, Bank of Finland Institute for Emerging Economies (BOFIT).
    6. Zsolt Darvas & György Szapáry, 2006. "Business Cycle Synchronization in the Enlarged EU," Working Papers 0604, Department of Mathematical Economics and Economic Analysis, Corvinus University of Budapest.
    7. Stelios Bekiros & Duc Khuong Nguyen & Gazi Salah Uddin & Bo Sjö, 2014. "Business Cycle (De)Synchronization in the Aftermath of the Global Financial Crisis: Implications for the Euro Area," Working Papers 2014-437, Department of Research, Ipag Business School.
    8. Carlo Di Giorgio, 2016. "Business Cycle Synchronization of CEECs with the Euro Area: A Regime Switching Approach," Journal of Common Market Studies, Wiley Blackwell, vol. 54(2), pages 284-300, March.
    9. Michael J. Artis & Jarko Fidrmuc & Johann Scharler, 2008. "The transmission of business cycles Implications for EMU enlargement1," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 16(3), pages 559-582, July.
    10. Camacho, Maximo & Perez-Quiros, Gabriel & Saiz, Lorena, 2008. "Do European business cycles look like one?," Journal of Economic Dynamics and Control, Elsevier, vol. 32(7), pages 2165-2190, July.
    11. Macchiarelli, Corrado, 2013. "GDP-Inflation cyclical similarities in the CEE countries and the euro area," Working Paper Series 1552, European Central Bank.
    12. Wasim Ahmad & N. Bhanumurthy & Sanjay Sehgal, 2015. "Regime dependent dynamics and European stock markets: Is asset allocation really possible?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 42(1), pages 77-107, February.
    13. Martin Gächter & Aleksandra Riedl & Doris Ritzberger-Grünwald, 2013. "Business cycle convergence or decoupling? Economic adjustment of CESEE countries during the crisis," Chapters, in: Ewald Nowotny & Peter Mooslechner & Doris Ritzberger-Grünwald (ed.), A New Model for Balanced Growth and Convergence, chapter 10, pages 147-169, Edward Elgar Publishing.

  92. Òscar Jordà & Massimiliano Marcellino, 2004. "Time‐scale transformations of discrete time processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(6), pages 873-894, November.
    See citations under working paper version above.
  93. Massimiliano Marcellino, 2004. "Forecast Pooling for European Macroeconomic Variables," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(1), pages 91-112, February.

    Cited by:

    1. Maurin, Laurent & Drechsel, Katja, 2008. "Flow of conjunctural information and forecast of euro area economic activity," Working Paper Series 925, European Central Bank.
    2. Henzel, Steffen R. & Mayr, Johannes, 2013. "The mechanics of VAR forecast pooling—A DSGE model based Monte Carlo study," The North American Journal of Economics and Finance, Elsevier, vol. 24(C), pages 1-24.
    3. MOULIN, Laurent & SALTO, Matteo & SILVESTRINI, Andrea & VEREDAS, David, 2004. "Using intra annual information to forecast the annual state deficits : the case of France," LIDAM Discussion Papers CORE 2004048, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Fuentes, Julieta & Poncela, Pilar & Rodríguez, Julio, 2014. "Selecting and combining experts from survey forecasts," DES - Working Papers. Statistics and Econometrics. WS ws140905, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Davide De Gaetano, 2017. "Forecasting With Garch Models Under Structural Breaks: An Approach Based On Combinations Across Estimation Windows," Departmental Working Papers of Economics - University 'Roma Tre' 0219, Department of Economics - University Roma Tre.
    6. Giancarlo Lutero & Marco Marini, 2010. "Direct vs Indirect Forecasts of Foreign Trade Unit Value Indices," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 12(2-3), pages 73-96, October.
    7. Esteban Fernández-Vázquez & Blanca Moreno, 2017. "Entropy Econometrics for combining regional economic forecasts: A Data-Weighted Prior Estimator," Journal of Geographical Systems, Springer, vol. 19(4), pages 349-370, October.
    8. Nikolay Robinzonov & Klaus Wohlrabe, 2008. "Freedom of Choice in Macroeconomic Forecasting: An Illustration with German Industrial Production and Linear Models," ifo Working Paper Series 57, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    9. Mayr, Johannes, 2010. "Forecasting Macroeconomic Aggregates," Munich Dissertations in Economics 11140, University of Munich, Department of Economics.
    10. Sabaj, Ernil & Kahveci, Mustafa, 2018. "Forecasting tax revenues in an emerging economy: The case of Albania," MPRA Paper 84404, University Library of Munich, Germany.
    11. David E. Rapach & Jack K. Strauss, 2008. "Forecasting US employment growth using forecast combining methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(1), pages 75-93.
    12. Christian Kascha & Francesco Ravazzolo, 2008. "Combining inflation density forecasts," Working Paper 2008/22, Norges Bank.

  94. Michael Artis & Massimiliano Marcellino & Tommaso Proietti, 2004. "Dating Business Cycles: A Methodological Contribution with an Application to the Euro Area," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(4), pages 537-565, September.

    Cited by:

    1. Emilio Zanetti Chini, 2018. "Forecaster’s utility and forecasts coherence," CREATES Research Papers 2018-01, Department of Economics and Business Economics, Aarhus University.
    2. Poncela, Pilar & Ruiz Ortega, Esther, 2012. "More is not always better : back to the Kalman filter in dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS ws122317, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Connor Bryant & Bernd Süssmuth, 2019. "Is the Relationship of Wealth Inequality with the Real, Financial and Housing Cycle Country-Specific?," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 47(3), pages 323-341, September.
    4. Sergey V. Smirnov & Nikolai V. Kondrashov & Anna V. Petronevich, 2016. "Dating Cyclical Turning Points for Russia: Formal Methods and Informal Choices," HSE Working papers WP BRP 122/EC/2016, National Research University Higher School of Economics.
    5. Hideaki Hirata & M. Ayhan Kose & Christopher Otrok, 2013. "Regionalization vs. globalization," Working Papers 2013-002, Federal Reserve Bank of St. Louis.
    6. Michael Funke, 2005. "Inflation in Mainland China - Modelling a Roller Coaster Ride," Quantitative Macroeconomics Working Papers 20507, Hamburg University, Department of Economics.
    7. Danilo Leiva-Leon, 2014. "A New Approach to Infer Changes in the Synchronization of Business Cycle Phases," Staff Working Papers 14-38, Bank of Canada.
    8. Cesaroni, Tatiana & Maccini, Louis & Malgarini, Marco, 2011. "Business cycle stylized facts and inventory behaviour: New evidence for the Euro area," International Journal of Production Economics, Elsevier, vol. 133(1), pages 12-24, September.
    9. Roberto Casarin & Komla Mawulom Agudze & Monica Billio & Eric Girardin, 2014. "Growth-cycle phases in China�s provinces: A panel Markov-switching approach," Working Papers 2014:19, Department of Economics, University of Venice "Ca' Foscari".
    10. Christophe Planas & Werner Roeger & Alessandro Rossi, 2004. "How much has labour taxation contributed to European structural unemployment?," Econometrics 0408005, University Library of Munich, Germany.
    11. Marcelle Chauvet & James D. Hamilton, 2005. "Dating Business Cycle Turning Points," NBER Working Papers 11422, National Bureau of Economic Research, Inc.
    12. Tommaso Proietti, 2009. "On the Model-Based Interpretation of Filters and the Reliability of Trend-Cycle Estimates," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 186-208.
    13. Cecilia Frale & Massimiliano Marcellino & Gian Luigi Mazzi & Tommaso Proietti, 2009. "Survey Data as Coicident or Leading Indicators," Economics Working Papers ECO2009/19, European University Institute.
    14. Michael Artis & Marianne Sensier, 2011. "Tracking Unemployment in Wales through Recession and into Recovery," SERC Discussion Papers 0079, Centre for Economic Performance, LSE.
    15. Sonia de Lucas Santos & M. Jesús Delgado Rodríguez & Inmaculada Álvarez Ayuso & José Luis Cendejas Bueno, 2011. "Los ciclos económicos internacionales: antecedentes y revisión de la literatura," Cuadernos de Economía - Spanish Journal of Economics and Finance, Asociación Cuadernos de Economía, vol. 34(95), pages 73-84, Agosto.
    16. Yongsung Chang & Sunoong Hwang, 2011. "Asymmetric Phase Shifts in the U.S. Industrial Production Cycles," RCER Working Papers 564, University of Rochester - Center for Economic Research (RCER).
    17. Ritabrata Bose & Ashima Goyal, 2020. "Disaggregated Indian industrial cycles: A Spectral analysis," Indira Gandhi Institute of Development Research, Mumbai Working Papers 2020-033, Indira Gandhi Institute of Development Research, Mumbai, India.
    18. Christian Aßmann & Jens Hogrefe & Roman Liesenfeld, 2009. "The decline in German output volatility: a Bayesian analysis," Empirical Economics, Springer, vol. 37(3), pages 653-679, December.
    19. Danilo Leiva-Leon, 2017. "Measuring business cycles intra-synchronization in us: a regime-switching interdependence framework," Working Papers 1726, Banco de España.
    20. Guido Bulligan & Massimiliano Marcellino & Fabrizio Venditti, 2012. "Forecasting economic activity with higher frequency targeted predictors," Temi di discussione (Economic working papers) 847, Bank of Italy, Economic Research and International Relations Area.
    21. Italo Colantone & Alessia Matano & Paolo Naticchioni, 2018. "New Imported Inputs, Wages and Worker Mobility," BAFFI CAREFIN Working Papers 1877, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    22. Ferrara, Laurent & Darné, Olivier, 2009. "Identification of slowdowns and accelerations for the euro area economy," CEPR Discussion Papers 7376, C.E.P.R. Discussion Papers.
    23. Jokubaitis, Saulius & Celov, Dmitrij & Leipus, Remigijus, 2021. "Sparse structures with LASSO through principal components: Forecasting GDP components in the short-run," International Journal of Forecasting, Elsevier, vol. 37(2), pages 759-776.
    24. Beate Schirwitz, 2009. "A comprehensive German business cycle chronology," Empirical Economics, Springer, vol. 37(2), pages 287-301, October.
    25. Marianne Sensier & Michael Artis, 2016. "The Resilience of UK Regional Employment Cycles," Centre for Growth and Business Cycle Research Discussion Paper Series 229, Economics, The University of Manchester.
    26. Gogas, Periklis & Kothroulas, George, 2009. "Two speed Europe and business cycle synchronization in the European Union: The effect of the common currency," MPRA Paper 13909, University Library of Munich, Germany.
    27. Yasutomo Murasawa, 2014. "Measuring the natural rates, gaps, and deviation cycles," Empirical Economics, Springer, vol. 47(2), pages 495-522, September.
    28. Yasutomo Murasawa, 2016. "The Beveridge–Nelson decomposition of mixed-frequency series," Empirical Economics, Springer, vol. 51(4), pages 1415-1441, December.
    29. Bodunrin, Olalekan Samuel, 2023. "The cause and Interaction between banking crises and the business cycle," MPRA Paper 117955, University Library of Munich, Germany.
    30. Tommaso Proietti & Cecilia Frale, 2007. "New proposals for the quantification of qualitative survey data," CEIS Research Paper 98, Tor Vergata University, CEIS.
    31. Jamel Gatfaoui & Eric Girardin, 2015. "Comovement of Chinese provincial business cycles," Post-Print hal-01456105, HAL.
    32. Louise Holm, 2016. "The Swedish business cycle, 1969-2013," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2015(2), pages 1-22.
    33. Tom Engsted & Stig V. Møller & Magnus Sander, 2013. "Bond return predictability in expansions and recessions," CREATES Research Papers 2013-13, Department of Economics and Business Economics, Aarhus University.
    34. Siliverstovs Boriss, 2013. "Dating Business Cycles in Historical Perspective: Evidence for Switzerland," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 233(5-6), pages 661-679, October.
    35. Christian Melzer & Thorsten Neumann, 2009. "Monetary policy in the euro area - has it become more powerful on the road to EMU?," Applied Economics Letters, Taylor & Francis Journals, vol. 16(18), pages 1801-1804.
    36. Maximo Camacho & Gabriel Perez-Quiros & Lorena Saiz & Universidad de Murcia, 2006. "Do european business cycles look like one $\_?$," Computing in Economics and Finance 2006 175, Society for Computational Economics.
    37. Beate Schirwitz & Christian Seiler & Klaus Wohlrabe, 2009. "Regional business cycles in Germany - the dating problem," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 62(14), pages 24-31, July.
    38. Martínez-García, Enrique & Grossman, Valerie & Mack, Adrienne, 2015. "A contribution to the chronology of turning points in global economic activity (1980–2012)," Journal of Macroeconomics, Elsevier, vol. 46(C), pages 170-185.
    39. Sylvia Kaufmann, 2008. "Dating and forecasting turning points by Bayesian clustering with dynamic structure: A suggestion with an application to Austrian data," Working Papers 144, Oesterreichische Nationalbank (Austrian Central Bank).
    40. Amélie Charles & Olivier Darné, 2015. "Identifying and characterizing business and acceleration cycles of French jobseekers Identifying and characterizing business and acceleration cycles of French jobseekers," Working Papers hal-01160090, HAL.
    41. Soh, Ann-Ni, 2020. "A Review on the Leading Indicator Approach towards Economic Forecasting," MPRA Paper 103854, University Library of Munich, Germany.
    42. Ferrara, L. & Vigna, O., 2009. "Cyclical relationships between GDP and housing market in France: Facts and factors at play," Working papers 268, Banque de France.
    43. Heather M. Anderson & Mardi Dungey & Denise R Osborn & Farshid Vahid, 2010. "Financial Integration and the Construction of Historical Financial Data for the Euro Area," Centre for Growth and Business Cycle Research Discussion Paper Series 152, Economics, The University of Manchester.
    44. Heather Anderson & Mardi Dungey & Denise R. Osborn & Farshid Vahid, 2007. "Constructing Historical Euro Area Data," CAMA Working Papers 2007-18, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    45. Camacho, Maximo & Perez-Quiros, Gabriel & Saiz, Lorena, 2008. "Do European business cycles look like one?," Journal of Economic Dynamics and Control, Elsevier, vol. 32(7), pages 2165-2190, July.
    46. Pamphile MEZUI-MBENG, 2012. "Cycle Du Credit Et Cycle Des Affaires Dans Les Pays De La Cemac," Cahiers du CEREFIGE 1202, CEREFIGE (Centre Europeen de Recherche en Economie Financiere et Gestion des Entreprises), Universite de Lorraine, revised 2012.
    47. Marianne Sensier & Michael Artis, 2016. "The Resilience of Employment in Wales: Through Recession and into Recovery," Regional Studies, Taylor & Francis Journals, vol. 50(4), pages 586-599, April.
    48. Tommaso Proietti, 2009. "Structural Time Series Models for Business Cycle Analysis," Palgrave Macmillan Books, in: Terence C. Mills & Kerry Patterson (ed.), Palgrave Handbook of Econometrics, chapter 9, pages 385-433, Palgrave Macmillan.
    49. Klaus Wohlrabe, 2011. "Konstruktion von Indikatoren zur Analyse der wirtschaftlichen Aktivität in den Dienstleistungsbereichen," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 55.
    50. Craigwell, Roland & Maurin, Alain, 2007. "A sectoral analysis of Barbados’ GDP business cycle," MPRA Paper 33428, University Library of Munich, Germany.
    51. Iolanda Lo Cascio & Stephen Pollock, 2007. "Comparative Economic Cycles," Working Papers 599, Queen Mary University of London, School of Economics and Finance.
    52. Agnieszka Gehringer & Thomas Mayer, 2021. "Measuring the Business Cycle Chronology with a Novel Business Cycle Indicator for Germany," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(1), pages 71-89, April.
    53. Saulius Jokubaitis & Dmitrij Celov & Remigijus Leipus, 2019. "Sparse structures with LASSO through Principal Components: forecasting GDP components in the short-run," Papers 1906.07992, arXiv.org, revised Oct 2020.
    54. Michael Artis & Marianne Sensier, 2010. "Tracking Unemployment in the North West Through Recession and Forecasting Recovery," Centre for Growth and Business Cycle Research Discussion Paper Series 136, Economics, The University of Manchester.
    55. Periklis Gogas, 2013. "Business cycle synchronisation in the European Union: The effect of the common currency," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2013(1), pages 1-14.
    56. Jefferson A. Colombo & Renan X. Cortes & Fernando I. L. Cruz & Luis H. Z. Paese, 2018. "Building State-Level Business Cycle Tracer Tools: Evidence from a Large Emerging Economy," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(5), pages 14-30, May.
    57. Santos, Sonia de Lucas & Rodríguez, María Jesús Delgado & Ayuso, Inmaculada Álvarez, 2011. "Application of factor models for the identification of countries sharing international reference-cycles," Economic Modelling, Elsevier, vol. 28(6), pages 2424-2431.
    58. Beate Schirwitz, 2013. "Business Fluctuations, Job Flows and Trade Unions - Dynamics in the Economy," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 47.
    59. Wang, Xiaoyu & Sun, Yanlin & Peng, Bin, 2023. "Industrial linkage and clustered regional business cycles in China," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 59-72.
    60. Robert Lehmann & Wolfgang Nierhaus & Magnus Reif, 2016. "A Flash Estimate of Private Consumption in Germany," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 69(21), pages 36-41, November.
    61. Sylvia Kaufmann, 2016. "Hidden Markov models in time series, with applications in economics," Working Papers 16.06, Swiss National Bank, Study Center Gerzensee.
    62. Hasan Engin Duran & Ugo Fratesi, 2023. "Economic resilience and regionally differentiated cycles: Evidence from a turning point approach in Italy," Papers in Regional Science, Wiley Blackwell, vol. 102(2), pages 219-252, April.
    63. Bulligan, Guido & Marcellino, Massimiliano & Venditti, Fabrizio, 2015. "Forecasting economic activity with targeted predictors," International Journal of Forecasting, Elsevier, vol. 31(1), pages 188-206.
    64. Anna Solms & Bernd Süssmuth, 2022. "Business cycle characteristics of Mediterranean economies: a secular trend and cycle dynamics perspective," International Economics and Economic Policy, Springer, vol. 19(4), pages 825-862, October.
    65. Kosei Fukuda, 2008. "Differentiating between business cycles and growth cycles: evidence from 15 developed countries," Applied Economics, Taylor & Francis Journals, vol. 40(7), pages 875-883.

  95. Marcellino, Massimliano, 2004. "Forecasting EMU macroeconomic variables," International Journal of Forecasting, Elsevier, vol. 20(2), pages 359-372.
    See citations under working paper version above.
  96. Anindya Banerjee & Massimiliano Marcellino & Chiara Osbat, 2004. "Some cautions on the use of panel methods for integrated series of macroeconomic data," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 322-340, December.
    See citations under working paper version above.
  97. Jordà, Òscar & Marcellino, Massimiliano, 2003. "Modeling High-Frequency Foreign Exchange Data Dynamics," Macroeconomic Dynamics, Cambridge University Press, vol. 7(4), pages 618-635, September.

    Cited by:

    1. Helmut Herwartz, 2006. "Econometric analysis of high frequency data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 89-104, March.

  98. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2003. "Macroeconomic forecasting in the Euro area: Country specific versus area-wide information," European Economic Review, Elsevier, vol. 47(1), pages 1-18, February.
    See citations under working paper version above.
  99. Massimiliano Marcellino & Grayham E. Mizon & Hans-Martin Krolzig, 2002. "A Markov-switching vector equilibrium correction model of the UK labour market," Empirical Economics, Springer, vol. 27(2), pages 233-254.
    See citations under working paper version above.
  100. Marcellino, Massimiliano & Salmon, Mark, 2002. "Robust Decision Theory And The Lucas Critique," Macroeconomic Dynamics, Cambridge University Press, vol. 6(1), pages 167-185, February.

    Cited by:

    1. Pataracchia, B., 2011. "Ambiguity and Volatility : Asset Pricing Implications," Discussion Paper 2011-042, Tilburg University, Center for Economic Research.
    2. Ekaterina Pirozhkova, 2017. "Financial frictions and robust monetary policy in the models of New Keynesian framework," BCAM Working Papers 1701, Birkbeck Centre for Applied Macroeconomics.
    3. William A. Brock & Steven N. Durlauf, 2004. "Elements of a Theory of Design Limits to Optimal Policy," Manchester School, University of Manchester, vol. 72(s1), pages 1-18, September.
    4. Rodríguez Arnulfo & González Fidel & González García Jesús R., 2007. "Uncertainty about the Persistence of Cost-Push Shocks and the Optimal Reaction of the Monetary Authority," Working Papers 2007-05, Banco de México.
    5. Brock,W.A. & Durlauf,S.N., 2004. "Macroeconomics and model uncertainty," Working papers 20, Wisconsin Madison - Social Systems.
    6. Brock,W.A. & Durlauf,S.N. & West,K.D., 2004. "Model uncertainty and policy evaluation : some theory and empirics," Working papers 19, Wisconsin Madison - Social Systems.
    7. Katherine Moos, 2016. "The Transvaluation of the Theory of Economic Policy: The Lucas Critique Reconsidered," Working Papers 1603, New School for Social Research, Department of Economics.
    8. Brock,W.A. & Durlauf,S.N., 2004. "Local robustness analysis : theory and application," Working papers 22, Wisconsin Madison - Social Systems.
    9. Gonzalez F. & Rodriguez A. & Gonzalez-Garcia J.R., 2005. "Uncertainty about the Persistence of Periods with Large Price Shocks and the Optimal Reaction of the Monetary Authority," Computing in Economics and Finance 2005 402, Society for Computational Economics.
    10. Kenneth Kasa, 1999. "Model uncertainty, robust policies, and the value of commitment," Working Paper Series 99-14, Federal Reserve Bank of San Francisco.
    11. Gerlach-Kristen, Petra, 2006. "Internal and external shocks in Hong Kong: Empirical evidence and policy options," Economic Modelling, Elsevier, vol. 23(1), pages 56-75, January.

  101. Michael Artis & Massimiliano Marcellino, 2001. "Fiscal forecasting: The track record of the IMF, OECD and EC," Econometrics Journal, Royal Economic Society, vol. 4(1), pages 20-36.
    See citations under working paper version above.
  102. Massimiliano Marcellino & Grayham E. Mizon, 2001. "Small-system modelling of real wages, inflation, unemployment and output per capita in Italy 1970-1994," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 359-370.
    See citations under working paper version above.
  103. Marcellino, Massimiliano & Mizon, Grayham E., 2000. "Modelling shifts in the wage-price and unemployment-inflation relationships in Italy, Poland and the UK," Economic Modelling, Elsevier, vol. 17(3), pages 387-413, August.
    See citations under working paper version above.
  104. Massimiliano Marcellino, 2000. "Forecast Bias and MSFE Encompassing," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 62(4), pages 533-542, September.

    Cited by:

    1. Massimiliano Marcellino, 2008. "A linear benchmark for forecasting GDP growth and inflation?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 305-340.
    2. Michael Artis & Anindya Banerjee & Massimiliano Marcellino, "undated". "Factor forecasts for the UK," Working Papers 203, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    3. Kontogeorgos, Georgios & Lambrias, Kyriacos, 2019. "An analysis of the Eurosystem/ECB projections," Working Paper Series 2291, European Central Bank.

  105. Federico Bonaglia & Eliana La Ferrara & Massimiliano Marcellino, 2000. "Public Capital and Economic Performance: Evidence from Italy," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 59(2), pages 221-244, September.
    See citations under working paper version above.
  106. Marcellino, Massimiliano, 1999. "Some Consequences of Temporal Aggregation in Empirical Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 129-136, January.

    Cited by:

    1. Frédérick Demers & Annie De Champlain, 2005. "Forecasting Core Inflation in Canada: Should We Forecast the Aggregate or the Components?," Staff Working Papers 05-44, Bank of Canada.
    2. Qian, Hang, 2012. "Essays on statistical inference with imperfectly observed data," ISU General Staff Papers 201201010800003618, Iowa State University, Department of Economics.
    3. Antonio Diez de los Rios & Enrique Sentana, 2011. "Testing Uncovered Interest Parity: A Continuous‐Time Approach," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 52(4), pages 1215-1251, November.
    4. Massimiliano Marcellino, 2007. "Pooling‐Based Data Interpolation and Backdating," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(1), pages 53-71, January.
    5. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    6. Percoco, Marco, 2015. "Temporal aggregation and spatio-temporal traffic modeling," Journal of Transport Geography, Elsevier, vol. 46(C), pages 244-247.
    7. Chi-Young Choi & Nelson Mark & Donggyu Sul, 2004. "Unbiased Estimation of the Half-Life to PPP Convergence in Panel Data," NBER Working Papers 10614, National Bureau of Economic Research, Inc.
    8. Tilak Abeysinghe & Anthony S. Tay, 2000. "Dynamic Regressions with Variables Observed at Different Frequencies," Econometric Society World Congress 2000 Contributed Papers 0752, Econometric Society.
    9. Andrea, SILVESTRINI, 2005. "Temporal aggregaton of univariate linear time series models," Discussion Papers (ECON - Département des Sciences Economiques) 2005044, Université catholique de Louvain, Département des Sciences Economiques.
    10. Cecilia Frale & Libero Monteforte, "undated". "FaMIDAS: A Mixed Frequency Factor Model with MIDAS structure," Working Papers 3, Department of the Treasury, Ministry of the Economy and of Finance.
    11. Hanan Naser, 2015. "Estimating and forecasting Bahrain quarterly GDP growth using simple regression and factor-based methods," Empirical Economics, Springer, vol. 49(2), pages 449-479, September.
    12. Maria Nikoloudaki & Dikaios Tserkezos, 2008. "Temporal Aggregation Effects in Choosing the Optimal Lag Order in Stable ARMA Models: Some Monte Carlo Results," Working Papers 0822, University of Crete, Department of Economics.
    13. Cláudia Duarte, 2015. "Covariate-augmented unit root tests with mixed-frequency data," Working Papers w201507, Banco de Portugal, Economics and Research Department.
    14. Libero Monteforte & Valentina Raponi, 2019. "Short‐term forecasts of economic activity: Are fortnightly factors useful?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(3), pages 207-221, April.
    15. Girardin, Eric & Liu, Zhenya, 2007. "The financial integration of China: New evidence on temporally aggregated data for the A-share market," China Economic Review, Elsevier, vol. 18(3), pages 354-371.
    16. Gaston, Noel & Rajaguru, Gulasekaran, 2013. "How an export boom affects unemployment," Economic Modelling, Elsevier, vol. 30(C), pages 343-355.
    17. Antonio Diez de los Rios, 2013. "A New Linear Estimator for Gaussian Dynamic Term Structure Models," Staff Working Papers 13-10, Bank of Canada.
    18. Christian Mueller, 2006. "Testing Temporal Disaggregation," KOF Working papers 06-134, KOF Swiss Economic Institute, ETH Zurich.
    19. Massimiliano Marcellino, 2008. "A linear benchmark for forecasting GDP growth and inflation?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(4), pages 305-340.
    20. Hui Jun ZHANG & Jean-Marie DUFOUR & John W. GALBRAITH, 2013. "Exchange Rates and Commodity Prices : Measuring Causality at Multiple Horizons," Cahiers de recherche 14-2013, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    21. Stefan J. Hock & Sascha Raithel, 2020. "Managing Negative Celebrity Endorser Publicity: How Announcements of Firm (Non)Responses Affect Stock Returns," Management Science, INFORMS, vol. 66(3), pages 1473-1495, March.
    22. Markus Eberhardt & Francis Teal, 2011. "Econometrics For Grumblers: A New Look At The Literature On Cross‐Country Growth Empirics," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 109-155, February.
    23. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2014. "Combining distributions of real-time forecasts: An application to U.S. growth," Research Memorandum 027, Maastricht University, Graduate School of Business and Economics (GSBE).
    24. Mamingi Nlandu, 2017. "Beauty and Ugliness of Aggregation over Time: A Survey," Review of Economics, De Gruyter, vol. 68(3), pages 205-227, December.
    25. Sbrana, Giacomo & Silvestrini, Andrea, 2013. "Aggregation of exponential smoothing processes with an application to portfolio risk evaluation," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1437-1450.
    26. Peter Fuleky & Carl Bonham, 2010. "Forecasting Based on Common Trends in Mixed Frequency Samples," Working Papers 2010-17R1, University of Hawaii Economic Research Organization, University of Hawaii at Manoa, revised Jul 2013.
    27. Hecq, A.W. & Götz, T.B. & Urbain, J.R.Y.J., 2012. "Forecasting Mixed Frequency Time Series with ECM-MIDAS Models," Research Memorandum 012, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    28. Nicholas Taylor, 2008. "The predictive value of temporally disaggregated volatility: evidence from index futures markets," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(8), pages 721-742.
    29. Eric Ghysels & J. Isaac Miller, 2014. "On the Size Distortion from Linearly Interpolating Low-frequency Series for Cointegration Tests," Working Papers 1403, Department of Economics, University of Missouri.
    30. Christian, Müller, 2011. "The forward-bias puzzle: Still unsolved," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(4), pages 605-610, October.
    31. Byeongchan Seong & Sung K. Ahn & Peter Zadrozny, 2007. "Cointegration Analysis with Mixed-Frequency Data," CESifo Working Paper Series 1939, CESifo.
    32. Guerino Ardizzi & Simone Emiliozzi & Juri Marcucci & Libero Monteforte, 2019. "News and consumer card payments," Temi di discussione (Economic working papers) 1233, Bank of Italy, Economic Research and International Relations Area.
    33. Jesús Otero & Theodore Panagiotidis & Georgios Papapanagiotou, 2022. "Multivariate Cointegration and Temporal Aggregation: Some Further Simulation Results," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 59-70, January.
    34. Foroni, Claudia & Marcellino, Massimiliano & Schumacher, Christian, 2011. "U-MIDAS: MIDAS regressions with unrestricted lag polynomials," Discussion Paper Series 1: Economic Studies 2011,35, Deutsche Bundesbank.
    35. Carlomagno, Guillermo & Fornero, Jorge & Sansone, Andrés, 2023. "A proposal for constructing and evaluating core inflation measures," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 4(3).
    36. Guillermo Carlomagno & Jorge Fornero & Andrés Sansone, 2021. "Toward a general framework for constructing and evaluating core inflation measures," Working Papers Central Bank of Chile 913, Central Bank of Chile.
    37. Reinhard Ellwanger, Stephen Snudden, 2021. "Predictability of Aggregated Time Series," LCERPA Working Papers bm0127, Laurier Centre for Economic Research and Policy Analysis.
    38. Ghysels, Eric & Miller, J. Isaac, 2013. "Testing for Cointegration with Temporally Aggregated and Mixed-frequency Time Series," CEPR Discussion Papers 9654, C.E.P.R. Discussion Papers.
    39. Christian M. Hafner, 2004. "Temporal aggregation of multivariate GARCH processes," Econometric Society 2004 North American Winter Meetings 538, Econometric Society.
    40. Bacchiocchi, Emanuele & Bastianin, Andrea & Missale, Alessandro & Rossi, Eduardo, 2016. "Structural analysis with mixed frequencies: monetary policy, uncertainty and gross capital flows," Working Papers 2016-04, Joint Research Centre, European Commission.
    41. Peter Fuleky & Carl S. Bonham, 2013. "Forecasting with Mixed Frequency Samples: The Case of Common Trends," Working Papers 201305, University of Hawaii at Manoa, Department of Economics.
    42. Mohammadipour, Maryam & Boylan, John E., 2012. "Forecast horizon aggregation in integer autoregressive moving average (INARMA) models," Omega, Elsevier, vol. 40(6), pages 703-712.
    43. Tilak Abeysinghe & Gulasekaran Rajaguru, 2004. "Temporal aggregation, causality distortions and a sign rule," Econometric Society 2004 Australasian Meetings 73, Econometric Society.
    44. Xing Jin & LepingWang & JunYu, 2007. "Temporal Aggregation and Risk-Return Relation," Finance Working Papers 21917, East Asian Bureau of Economic Research.
    45. Stephen M. Shellman & Brandon M. Stewart, 2007. "Political Persecution or Economic Deprivation? A Time-Series Analysis of Haitian Exodus, 1990—2004," Conflict Management and Peace Science, Peace Science Society (International), vol. 24(2), pages 121-137, April.
    46. Massimiliano Marcellino & Grayham E. Mizon, "undated". "Small system modelling of real wages, inflation, unemployment and output per capita in Italy 1970-1994," Working Papers 188, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    47. Andrea Silvestrini & David Veredas, 2008. "Temporal aggregation of univariate and multivariate time series models: A survey," Temi di discussione (Economic working papers) 685, Bank of Italy, Economic Research and International Relations Area.
    48. Huang, Yu-Lieh, 2012. "Measuring business cycles: A temporal disaggregation model with regime switching," Economic Modelling, Elsevier, vol. 29(2), pages 283-290.
    49. Cartwright, Phillip A. & Riabko, Natalija, 2015. "Measuring the effect of oil prices on wheat futures prices," Research in International Business and Finance, Elsevier, vol. 33(C), pages 355-369.
    50. Víctor Gómez & Félix Aparicio‐Pérez, 2009. "A new state–space methodology to disaggregate multivariate time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(1), pages 97-124, January.
    51. Marcellino, Massimiliano & Mizon, Grayham E., 2000. "Modelling shifts in the wage-price and unemployment-inflation relationships in Italy, Poland and the UK," Economic Modelling, Elsevier, vol. 17(3), pages 387-413, August.
    52. Oscar Jorda & Massimiliano Marcellino, "undated". "Stochastic Processes Subject To Time Scale Transformations: An Application To High-Frequency Fx Data," Department of Economics 00-02, California Davis - Department of Economics.
    53. Alfred A. Haug, 2002. "Temporal Aggregation and the Power of Cointegration Tests: a Monte Carlo Study," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(4), pages 399-412, September.
    54. Hubrich, Kirstin, 2005. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," International Journal of Forecasting, Elsevier, vol. 21(1), pages 119-136.
    55. Du, Yingxin & Ju, Jiandong & Ramirez, Carlos D. & Yao, Xi, 2017. "Bilateral trade and shocks in political relations: Evidence from China and some of its major trading partners, 1990–2013," Journal of International Economics, Elsevier, vol. 108(C), pages 211-225.
    56. Uwe Hassler, 2011. "Estimation of fractional integration under temporal aggregation," Post-Print hal-00815563, HAL.
    57. José Casals & Miguel Jerez & Sonia Sotoca, 2009. "Modelling and forecasting time series sampled at different frequencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(4), pages 316-342.
    58. Enrique M. Quilis, 2018. "Temporal disaggregation of economic time series: The view from the trenches," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 447-470, November.
    59. Alejandro Vicondoa & Andrea Gazzani, 2020. "Bridge Proxy-SVAR: Estimating the Macroeconomic Effects of Shocks Identified at High-Frequency," Documentos de Trabajo 533, Instituto de Economia. Pontificia Universidad Católica de Chile..
    60. Ellwanger, Reinhard & Snudden, Stephen, 2023. "Forecasts of the real price of oil revisited: Do they beat the random walk?," Journal of Banking & Finance, Elsevier, vol. 154(C).
    61. J. Isaac Miller, 2014. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Journal of Financial Econometrics, Oxford University Press, vol. 12(3), pages 584-614.
    62. Foroni, Claudia & Marcellino, Massimiliano & Stevanović, Dalibor, 2018. "Mixed frequency models with MA components," Discussion Papers 02/2018, Deutsche Bundesbank.
    63. Tierney, Heather L.R. & Kim, Jiyoon (June) & Nazarov, Zafar, 2018. "The Effects of Temporal Aggregation on Search Engine Data," MPRA Paper 84474, University Library of Munich, Germany.
    64. Marco Percoco, 2007. "Evaluating forecasting accuracy of the temporally aggregated space-time autoregressive model," Applied Economics Letters, Taylor & Francis Journals, vol. 14(9), pages 637-641.
    65. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2015. "Testing for Granger causality in large mixed-frequency VARs," Discussion Papers 45/2015, Deutsche Bundesbank.
    66. Bacchiocchi, Emanuele & Bastianin, Andrea & Missale, Alessandro & Rossi, Eduardo, 2020. "Structural analysis with mixed-frequency data: A model of US capital flows," Economic Modelling, Elsevier, vol. 89(C), pages 427-443.
    67. Dudda, Tom L. & Klein, Tony & Nguyen, Duc Khuong & Walther, Thomas, 2022. "Common Drivers of Commodity Futures?," QBS Working Paper Series 2022/05, Queen's University Belfast, Queen's Business School.
    68. Michael Thornton & Marcus Chambers, 2016. "Continuous Time ARMA Processes: Discrete Time Representation and Likelihood Evaluation," Discussion Papers 16/10, Department of Economics, University of York.
    69. Müller, Christian, 2012. "A new interpretation of known facts: The case of two-way causality between trading and volatility," Economic Modelling, Elsevier, vol. 29(3), pages 664-670.
    70. McCrorie, J. Roderick & Chambers, Marcus J., 2006. "Granger causality and the sampling of economic processes," Journal of Econometrics, Elsevier, vol. 132(2), pages 311-336, June.
    71. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    72. Jung, Young Cheol & Das, Anupam & McFarlane, Adian, 2020. "The asymmetric relationship between the oil price and the US-Canada exchange rate," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 198-206.
    73. Mr. Tao Wu & Mr. Michele Cavallo, 2012. "Measuring Oil-Price Shocks Using Market-Based Information," IMF Working Papers 2012/019, International Monetary Fund.
    74. Chambers, MJ, 2016. "The Effects of Sampling Frequency on Detrending Methods for Unit Root Tests," Economics Discussion Papers 16062, University of Essex, Department of Economics.
    75. Sebastian Rondeau, 2012. "Sources of Fluctuations in Emerging Markets: Structural Estimation with Mixed Frequency Data," 2012 Meeting Papers 1156, Society for Economic Dynamics.
    76. Piergiorgio Alessandri & Andrea Gazzani & Alejandro Vicondoa, 2021. "The Real Effects of Financial Uncertainty Shocks: A Daily Identification Approach," Documentos de Trabajo 559, Instituto de Economia. Pontificia Universidad Católica de Chile..
    77. Jose Ignacio Lopez & Virginia Olivella, 2018. "The importance of intangible capital for the transmission of financial shocks," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 30, pages 223-238, October.
    78. Gabriel Pons Rotger, 2000. "Temporal Aggregation and Ordinary Least Squares Estimation of Cointegrating Regressions," Econometric Society World Congress 2000 Contributed Papers 1317, Econometric Society.
    79. Nuttanan Wichitaksorn, 2020. "Analyzing and Forecasting Thai Macroeconomic Data using Mixed-Frequency Approach," PIER Discussion Papers 146, Puey Ungphakorn Institute for Economic Research.
    80. J. Isaac Miller & Xi Wang, 2016. "Implementing Residual-Based KPSS Tests for Cointegration with Data Subject to Temporal Aggregation and Mixed Sampling Frequencies," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(6), pages 810-824, November.
    81. Dimitra Papadovasilaki & Federico Guerrero & Rattaphon Wuthisatian & Bhraman Gulati, 2022. "The 1920s technological revolution and the crash of 1929: the role of RCA, DuPont, General Motors, and Union Carbide," SN Business & Economics, Springer, vol. 2(5), pages 1-22, May.
    82. Müller-Kademann Christian, 2015. "Internal Validation of Temporal Disaggregation: A Cloud Chamber Approach," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 235(3), pages 298-319, June.
    83. J.I.Lopez & V. Olivella Moppett, 2014. "Financial Shocks and the Cyclical Behavior of Skilled and Unskilled Unemployment," Working papers 496, Banque de France.
    84. Bahar Şen Doğan & Murat Midiliç, 2019. "Forecasting Turkish real GDP growth in a data-rich environment," Empirical Economics, Springer, vol. 56(1), pages 367-395, January.
    85. Kosei Fukuda, 2009. "Related-variables selection in temporal disaggregation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(4), pages 343-357.
    86. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2013. "Testing for Granger Causality with Mixed Frequency Data," CEPR Discussion Papers 9655, C.E.P.R. Discussion Papers.
    87. Phillip A. Cartwright & Natalija Riabko, 2019. "Do spot food commodity and oil prices predict futures prices?," Review of Quantitative Finance and Accounting, Springer, vol. 53(1), pages 153-194, July.
    88. Gulasekaran Rajaguru & Reza Siregar, 2002. "Sources of Variations Between The Inflation Rates of Korea, Thailand and Indonesia During The Post-1997 Crisis," Centre for International Economic Studies Working Papers 2002-29, University of Adelaide, Centre for International Economic Studies.
    89. Mokinski, Frieder & Wölfing, Nikolas, 2013. "The effect of regulatory scrutiny asymmetric cost pass-through in power wholesale and its end," ZEW Discussion Papers 13-055, ZEW - Leibniz Centre for European Economic Research.
    90. Raffaele Mattera & Michelangelo Misuraca & Maria Spano & Germana Scepi, 2023. "Mixed frequency composite indicators for measuring public sentiment in the EU," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2357-2382, June.
    91. Jin, Xing & Wang, Leping & Yu, Jun, 2007. "Temporal aggregation and risk-return relation," Finance Research Letters, Elsevier, vol. 4(2), pages 104-115, June.
    92. Oscar Jorda & Massimiliano Marcellino, 2003. "Time-Scale Transformations of Discrete-Time Processes," Working Papers 65, University of California, Davis, Department of Economics.
    93. Peter Vlaar & Ard den Reijer, 2004. "Forecasting inflation: An art as well as a science!," Computing in Economics and Finance 2004 148, Society for Computational Economics.
    94. Rajaguru, Gulasekaran & Abeysinghe, Tilak, 2008. "Temporal aggregation, cointegration and causality inference," Economics Letters, Elsevier, vol. 101(3), pages 223-226, December.
    95. J. Isaac Miller, 2014. "Simple Robust Tests for the Specification of High-Frequency Predictors of a Low-Frequency Series," Working Papers 1412, Department of Economics, University of Missouri.
    96. Qian, Hang, 2013. "Vector Autoregression with Mixed Frequency Data," MPRA Paper 47856, University Library of Munich, Germany.
    97. Gulasekaran Rajaguru & Tilak Abeysinghe, 2004. "Quarterly real GDP estimates for China and ASEAN4 with a forecast evaluation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 431-447.
    98. E Andreou & A Pelloni & M Sensier, 2003. "The effect of nominal shock uncertainty on output growth," Centre for Growth and Business Cycle Research Discussion Paper Series 40, Economics, The University of Manchester.
    99. M. Chudý & S. Karmakar & W. B. Wu, 2020. "Long-term prediction intervals of economic time series," Empirical Economics, Springer, vol. 58(1), pages 191-222, January.
    100. Bilson, Chris & Brailsford, Tim & Rajaguru, Gulasekaran, 2022. "Covered interest rate parity deviations in the Asia-Pacific," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 77(C).
    101. Michael O'Neill & Gulasekaran Rajaguru, 2020. "A response surface analysis of critical values for the lead‐lag ratio with application to high frequency and non‐synchronous financial data," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(4), pages 3979-3990, December.
    102. Chambers, MJ & McCrorie, JR & Thornton, MA, 2017. "Continuous Time Modelling Based on an Exact Discrete Time Representation," Economics Discussion Papers 20497, University of Essex, Department of Economics.
    103. Rajaguru, Gulasekaran, 2004. "Impact of systematic sampling on causality in the presence of unit roots," Economics Letters, Elsevier, vol. 84(1), pages 127-132, July.
    104. Andrea Giovanni Gazzani & Alejandro Vicondoa, 2019. "Proxy-SVAR as a Bridge for Identification with Higher Frequency Data," 2019 Meeting Papers 855, Society for Economic Dynamics.
    105. Phillip A. Cartwright & Natalija Riabko, 2016. "Further evidence on the explanatory power of spot food and energy commodities market prices for futures prices," Review of Quantitative Finance and Accounting, Springer, vol. 47(3), pages 579-605, October.
    106. Massimiliano Marcellino & Grayham E. Mizon, 2000. "Wages, Prices, Productivity, Inflation and Unemployment in Italy 1970-1994," Econometric Society World Congress 2000 Contributed Papers 0911, Econometric Society.
    107. Götz, Thomas B. & Hecq, Alain & Urbain, Jean-Pierre, 2016. "Combining forecasts from successive data vintages: An application to U.S. growth," International Journal of Forecasting, Elsevier, vol. 32(1), pages 61-74.
    108. Stephen Nemeth & Brian Lai, 2022. "When do natural disasters lead to negotiations in a civil war?," Journal of Peace Research, Peace Research Institute Oslo, vol. 59(1), pages 28-42, January.
    109. Bente Halvorsen & Bodil M. Larsen, 2008. "The Role of Heterogeneous Demand for Temporal and Structural Aggregation Bias," Discussion Papers 537, Statistics Norway, Research Department.
    110. Helmut Luetkepohl, 2009. "Forecasting Aggregated Time Series Variables: A Survey," Economics Working Papers ECO2009/17, European University Institute.
    111. Jewitt, Giles & Roderick McCrorie, J., 2005. "Computing estimates of continuous time macroeconometric models on the basis of discrete data," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 397-416, April.
    112. Bartsch, Zachary, 2019. "Economic policy uncertainty and dollar-pound exchange rate return volatility," Journal of International Money and Finance, Elsevier, vol. 98(C), pages 1-1.
    113. George Athanasopoulos & Puwasala Gamakumara & Anastasios Panagiotelis & Rob J Hyndman & Mohamed Affan, 2019. "Hierarchical Forecasting," Monash Econometrics and Business Statistics Working Papers 2/19, Monash University, Department of Econometrics and Business Statistics.
    114. Piergiorgio Alessandri & Andrea Gazzani & Alejandro Vicondoa, 2023. "Are the Effects of Uncertainty Shocks Big or Small?," Working Papers 244, Red Nacional de Investigadores en Economía (RedNIE).
    115. Claudia Foroni & Massimiliano Marcellino, 2014. "Mixed frequency structural VARs," Working Paper 2014/01, Norges Bank.
    116. Gerard J. Tellis & Philip Hans Franses, 2006. "Optimal Data Interval for Estimating Advertising Response," Marketing Science, INFORMS, vol. 25(3), pages 217-229, 05-06.
    117. Maria Elena Bontempi & Roberto Golinelli, 2012. "The effect of neglecting the slope parameters’ heterogeneity on dynamic models of corporate capital structure," Quantitative Finance, Taylor & Francis Journals, vol. 12(11), pages 1733-1751, November.
    118. Giusto Andrea & İşcan Talan B., 2018. "The Rescaled VAR Model with an Application to Mixed-Frequency Macroeconomic Forecasting," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(4), pages 1-16, September.
    119. Bent Jesper Christensen & Olaf Posch & Michel van der Wel, 2014. "Estimating Dynamic Equilibrium Models Using Mixed Frequency Macro and Financial Data," CESifo Working Paper Series 5030, CESifo.
    120. J. Isaac Miller, 2016. "Conditionally Efficient Estimation of Long-Run Relationships Using Mixed-Frequency Time Series," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 1142-1171, June.
    121. Michael A. Thornton & Marcus J. Chambers, 2013. "Temporal aggregation in macroeconomics," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 13, pages 289-310, Edward Elgar Publishing.
    122. Götz, T.B. & Hecq, A.W. & Urbain, J.R.Y.J., 2013. "Testing for common cycles in non-stationary VARs with varied frecquency data," Research Memorandum 002, Maastricht University, Graduate School of Business and Economics (GSBE).
    123. Gulasekaran Rajaguru & Michael O’Neill & Tilak Abeysinghe, 2018. "Does Systematic Sampling Preserve Granger Causality with an Application to High Frequency Financial Data?," Econometrics, MDPI, vol. 6(2), pages 1-24, June.
    124. Franses, Ph.H.B.F., 2016. "Yet another look at MIDAS regression," Econometric Institute Research Papers EI2016-32, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    125. Reinhard Ellwanger, Stephen Snudden, Lenin Arango-Castillo, 2023. "Seize the Last Day: Period-End-Point Sampling for Forecasts of Temporally Aggregated Data," LCERPA Working Papers bm0142, Laurier Centre for Economic Research and Policy Analysis.
    126. Wichitaksorn, Nuttanan, 2022. "Analyzing and forecasting Thai macroeconomic data using mixed-frequency approach," Journal of Asian Economics, Elsevier, vol. 78(C).
    127. Uwe Hassler, 2013. "Effect of temporal aggregation on multiple time series in the frequency domain," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(5), pages 562-573, September.

Chapters

  1. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2016. "An Overview of the Factor-augmented Error-Correction Model," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 3-41, Emerald Group Publishing Limited.
    See citations under working paper version above.
  2. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Chapter 4 Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Frontiers of Economics and Globalization, in: Forecasting in the Presence of Structural Breaks and Model Uncertainty, pages 149-194, Emerald Group Publishing Limited.

    Cited by:

    1. Boniface Yemba & Yi Duan & Nabaneeta Biswas, 2023. "Government spending news and stock price index," Economics Bulletin, AccessEcon, vol. 43(4), pages 1816-1841.
    2. Fu, Zhonghao & Hong, Yongmiao & Wang, Xia, 2023. "Testing for structural changes in large dimensional factor models via discrete Fourier transform," Journal of Econometrics, Elsevier, vol. 233(1), pages 302-331.
    3. Yemba, Boniface P. & Otunuga, Olusegun Michael & Tang, Biyan & Biswas, Nabaneeta, 2023. "Nowcasting of the Short-run Euro-Dollar Exchange Rate with Economic Fundamentals and Time-varying Parameters," Finance Research Letters, Elsevier, vol. 52(C).
    4. Fu, Zhonghao & Hong, Yongmiao & Su, Liangjun & Wang, Xia, 2023. "Specification tests for time-varying coefficient models," Journal of Econometrics, Elsevier, vol. 235(2), pages 720-744.
    5. Ma, Chenchen & Tu, Yundong, 2023. "Group fused Lasso for large factor models with multiple structural breaks," Journal of Econometrics, Elsevier, vol. 233(1), pages 132-154.

  3. Marcellino, Massimiliano, 2006. "Leading Indicators," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 16, pages 879-960, Elsevier.

    Cited by:

    1. Heij, Christiaan & van Dijk, Dick & Groenen, Patrick J.F., 2011. "Real-time macroeconomic forecasting with leading indicators: An empirical comparison," International Journal of Forecasting, Elsevier, vol. 27(2), pages 466-481, April.
    2. Carriero, Andrea & Marcellino, Massimiliano, 2007. "A comparison of methods for the construction of composite coincident and leading indexes for the UK," International Journal of Forecasting, Elsevier, vol. 23(2), pages 219-236.
    3. Ciccarelli, Matteo & Mojon, Benoît, 2006. "Global Inflation," Kiel Working Papers 1337, Kiel Institute for the World Economy (IfW Kiel).
    4. Knut Are Aastveit & Karsten R. Gerdrup & Anne Sofie Jore & Leif Anders Thorsrud, 2011. "Nowcasting GDP in real-time: A density combination approach," Working Paper 2011/11, Norges Bank.
    5. H. Burcu Gurcihan & Gonul Sengul & Arzu Yavuz, 2013. "A Quest for Leading Indicators of the Turkish Unemployment Rate," Working Papers 1341, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    6. Aastveit, Knut Are & Anundsen, André K. & Herstad, Eyo I., 2019. "Residential investment and recession predictability," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1790-1799.
    7. Travaglini, Guido, 2011. "Climate change: where is the hockey stick? evidence from millennial-scale reconstructed and updated temperature time series," MPRA Paper 35565, University Library of Munich, Germany.
    8. Massimiliano Serati & Matteo Manera & Michele Plotegher, 2008. "Modelling electricity prices: from the state of the art to a draft of a new proposal," LIUC Papers in Economics 210, Cattaneo University (LIUC).
    9. Nikolay Robinzonov & Klaus Wohlrabe, 2008. "Freedom of Choice in Macroeconomic Forecasting: An Illustration with German Industrial Production and Linear Models," ifo Working Paper Series 57, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    10. Bram van Os & Dick van Dijk, 2020. "Accelerating Peak Dating in a Dynamic Factor Markov-Switching Model," Tinbergen Institute Discussion Papers 20-057/VI, Tinbergen Institute, revised 14 Dec 2020.
    11. Michael T. Owyang & David E. Rapach & Howard J. Wall, 2008. "States and the business cycle," Working Papers 2007-050, Federal Reserve Bank of St. Louis.
    12. Rossi, Barbara, 2013. "Advances in Forecasting under Instability," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1203-1324, Elsevier.
    13. Pablo Galaso & Sandra Rodríguez, 2016. "A composite leading cycle indicator for Uruguay," Estudios Regionales en Economía, Población y Desarrollo. Cuadernos de Trabajo de la Universidad Autónoma de Ciudad Juárez. 31, Cuerpo Académico 41 de la Universidad Autónoma de Ciudad Juárez, revised 01 Feb 2016.
    14. Máximo Camacho & Gonzalo Palmieri, 2021. "Evaluating the OECD’s main economic indicators at anticipating recessions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 80-93, January.
    15. Alessandro Girardi & Marco Ventura, 2021. "Measuring credit crunch in Italy: evidence from a survey-based indicator," Annals of Operations Research, Springer, vol. 299(1), pages 567-592, April.
    16. Heather M. Anderson & Mardi Dungey & Denise R Osborn & Farshid Vahid, 2010. "Financial Integration and the Construction of Historical Financial Data for the Euro Area," Centre for Growth and Business Cycle Research Discussion Paper Series 152, Economics, The University of Manchester.
    17. Barrera, Carlos, 2009. "Ciclos sectoriales de los negocios en el Perú e indicadores anticipados para el crecimiento del PBI no primario," Working Papers 2009-013, Banco Central de Reserva del Perú.
    18. Jason Angelopoulos & Costas I. Chlomoudis, 2017. "A Generalized Dynamic Factor Model for the U.S. Port Sector," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 67(1), pages 22-37, January-M.

Books

  1. Artis,Michael & Banerjee,Anindya & Marcellino,Massimiliano (ed.), 2006. "The Central and Eastern European Countries and the European Union," Cambridge Books, Cambridge University Press, number 9780521849548.

    Cited by:

    1. Kuhar, Aleš & Erjavec, Emil & Borovšak, Katarina, 2014. "Economic trends in the Slovenian food industry during the pre - and post EUaccession period," Agroeconomia Croatica, Croatian Society of Agricultural Economists, vol. 4(1), pages 1-10, July.
    2. Robert Lehmann, 2016. "Economic Growth and Business Cycle Forecasting at the Regional Level," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 65.
    3. E. Marrocu & R. Paci & S. Usai, 2010. "Productivity growth in the Old and New Europe: the role of agglomeration externalities," Working Paper CRENoS 201024, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    4. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Working Papers 334, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    5. Augusto Cerqua & Roberta Di Stefano & Guido Pellegrini, 2021. "What kind of region reaps the benefits of a currency union?," Working Papers 2/21, Sapienza University of Rome, DISS.
    6. Lombardi, Marco J. & Maier, Philipp, 2011. "Forecasting economic growth in the euro area during the Great Moderation and the Great Recession," Working Paper Series 1379, European Central Bank.
    7. Robert Lehmann & Klaus Wohlrabe, 2012. "Forecasting GDP at the Regional Level with Many Predictors," CESifo Working Paper Series 3956, CESifo.
    8. Christian Schulz, 2008. "Forecasting economic activity for Estonia : The application of dynamic principal component analyses," Bank of Estonia Working Papers 2008-02, Bank of Estonia, revised 30 Oct 2008.
    9. Sandra Eickmeier & Tim Ng, 2009. "Forecasting national activity using lots of international predictors: an application to New Zealand," Reserve Bank of New Zealand Discussion Paper Series DP2009/04, Reserve Bank of New Zealand.
    10. Johannes Tang Kristensen, 2012. "Factor-Based Forecasting in the Presence of Outliers: Are Factors Better Selected and Estimated by the Median than by The Mean?," CREATES Research Papers 2012-28, Department of Economics and Business Economics, Aarhus University.
    11. Mr. Daniel Leigh & Mr. Abdul d Abiad & Mr. Ashoka Mody, 2007. "International Finance and Income Convergence: Europe is Different," IMF Working Papers 2007/064, International Monetary Fund.
    12. Kelber, A., 2010. "Cohesion policy and the new Member States of the European Union," Quarterly selection of articles - Bulletin de la Banque de France, Banque de France, issue 19, pages 77-95, Autumn.
    13. Young Patricia T, 2010. "Captured by Business? Romanian Market Governance and the New Economic Elite," Business and Politics, De Gruyter, vol. 12(1), pages 1-40, April.
    14. Chen, Zhengyang & Valcarcel, Victor J., 2021. "Monetary transmission in money markets: The not-so-elusive missing piece of the puzzle," Journal of Economic Dynamics and Control, Elsevier, vol. 131(C).
    15. Daniel Montolio & Albert Solé‐Ollé, 2009. "Road investment and regional productivity growth: the effects of vehicle intensity and congestion," Papers in Regional Science, Wiley Blackwell, vol. 88(1), pages 99-118, March.
    16. Ki-Sik Hwang, 2008. "Sub-National Level Analysis on FDI Relocation towards Eastern Europe," International Area Studies Review, Center for International Area Studies, Hankuk University of Foreign Studies, vol. 11(1), pages 19-34, March.

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