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Michael Peter Clements

Not to be confused with: Michael A. Clemens

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.

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Clements, Michael P & Hendry, David F, 1996. "Intercept Corrections and Structural Change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 475-494, Sept.-Oct.

    Mentioned in:

    1. Forecasting GDP in the presence of breaks: when is the past is a good guide to the future?
      by bankunderground in Bank Underground on 2015-08-20 11:30:00
    2. Forecasting GDP in the presence of breaks: when is the past a good guide to the future?
      by Guest Author in The Big Picture on 2015-09-01 14:00:11
  2. Jennifer Castle & David Hendry & Michael P. Clements, 2014. "Robust Approaches to Forecasting," Economics Series Working Papers 697, University of Oxford, Department of Economics.

    Mentioned in:

    1. In all probability, economic forecasts are probably wrong
      by David F Hendry, Director, Economic Modelling, The Institute for New Economic Thinking at the Oxford Martin School at University of Oxford in The Conversation on 2014-07-18 17:06:35

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. Clements, Michael P & Smith, Jeremy, 1999. "A Monte Carlo Study of the Forecasting Performance of Empirical SETAR Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(2), pages 123-141, March-Apr.

    Mentioned in:

    1. A Monte Carlo study of the forecasting performance of empirical SETAR models (Journal of Applied Econometrics 1999) in ReplicationWiki ()
  2. Clements, Michael P & Hendry, David F, 1996. "Intercept Corrections and Structural Change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 475-494, Sept.-Oct.

    Mentioned in:

    1. Intercept corrections and structural change (Journal of Applied Econometrics 1996) in ReplicationWiki ()
  3. Michael P. Clements & David I. Harvey, 2010. "Forecast encompassing tests and probability forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(6), pages 1028-1062.

    Mentioned in:

    1. Forecast encompassing tests and probability forecasts (Journal of Applied Econometrics 2010) in ReplicationWiki ()
  4. Michael P. Clements & Ana Beatriz Galvao, 2009. "Forecasting US output growth using leading indicators: an appraisal using MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1187-1206.

    Mentioned in:

    1. Forecasting US output growth using leading indicators: an appraisal using MIDAS models (Journal of Applied Econometrics 2009) in ReplicationWiki ()
  5. Clements, Michael P & Hendry, David F, 1995. "Forecasting in Cointegration Systems," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 127-146, April-Jun.

    Mentioned in:

    1. Forecasting in cointegrated systems (Journal of Applied Econometrics 1995) in ReplicationWiki ()
  6. Fred Joutz & Michael P. Clements & Herman O. Stekler, 2007. "An evaluation of the forecasts of the federal reserve: a pooled approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 121-136.

    Mentioned in:

    1. An evaluation of the forecasts of the federal reserve: a pooled approach (Journal of Applied Econometrics 2007) in ReplicationWiki ()

Working papers

  1. Michael Clements & Robert W. Rich & Joseph Tracy, 2022. "Surveys of Professionals," Working Papers 22-13, Federal Reserve Bank of Cleveland.

    Cited by:

    1. Todd E. Clark & Gergely Ganics & Elmar Mertens, 2022. "Constructing Fan Charts from the Ragged Edge of SPF Forecasts," Working Papers 22-36, Federal Reserve Bank of Cleveland.

  2. Cepni, Oguzhan & Clements, Michael P., 2021. "How Local is the Local Inflation Factor? Evidence from Emerging European Countries," Working Papers 8-2021, Copenhagen Business School, Department of Economics.

    Cited by:

    1. Jiawen Luo & Shengjie Fu & Oguzhan Cepni & Rangan Gupta, 2024. "Climate Risks and Forecastability of US Inflation: Evidence from Dynamic Quantile Model Averaging," Working Papers 202420, University of Pretoria, Department of Economics.

  3. 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.

    Cited by:

    1. Janczura, Joanna & Wójcik, Edyta, 2022. "Dynamic short-term risk management strategies for the choice of electricity market based on probabilistic forecasts of profit and risk measures. The German and the Polish market case study," Energy Economics, Elsevier, vol. 110(C).
    2. Joanna Janczura & Andrzej Puć, 2023. "ARX-GARCH Probabilistic Price Forecasts for Diversification of Trade in Electricity Markets—Variance Stabilizing Transformation and Financial Risk-Minimizing Portfolio Allocation," Energies, MDPI, vol. 16(2), pages 1-28, January.
    3. Heymann, Fabian & Milojevic, Tatjana & Covatariu, Andrei & Verma, Piyush, 2023. "Digitalization in decarbonizing electricity systems – Phenomena, regional aspects, stakeholders, use cases, challenges and policy options," Energy, Elsevier, vol. 262(PB).
    4. Nie, Yan & Zhang, Guoxing & Zhong, Luhao & Su, Bin & Xi, Xi, 2024. "Urban‒rural disparities in household energy and electricity consumption under the influence of electricity price reform policies," Energy Policy, Elsevier, vol. 184(C).
    5. Spiliotis, Evangelos & Petropoulos, Fotios, 2024. "On the update frequency of univariate forecasting models," European Journal of Operational Research, Elsevier, vol. 314(1), pages 111-121.
    6. Wesley Marcos Almeida & Claudimar Pereira Veiga, 2023. "Does demand forecasting matter to retailing?," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(2), pages 219-232, June.
    7. Elalem, Yara Kayyali & Maier, Sebastian & Seifert, Ralf W., 2023. "A machine learning-based framework for forecasting sales of new products with short life cycles using deep neural networks," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1874-1894.
    8. Anna Sznajderska & Alfred A. Haug, 2023. "Bayesian VARs of the U.S. economy before and during the pandemic," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(2), pages 211-236, June.
    9. Michael Pedersen, 2024. "Judgment in macroeconomic output growth predictions: Efficiency, accuracy and persistence," Papers 2404.04105, arXiv.org.
    10. Jun Meng & Jingfang Fan & Uma S. Bhatt & Jürgen Kurths, 2023. "Arctic weather variability and connectivity," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    11. Ramos, Paulo Vitor B. & Villela, Saulo Moraes & Silva, Walquiria N. & Dias, Bruno H., 2023. "Residential energy consumption forecasting using deep learning models," Applied Energy, Elsevier, vol. 350(C).
    12. Marek Kwas & Alessia Paccagnini & Michal Rubaszek, 2020. "Common factors and the dynamics of cereal prices. A forecasting perspective," CAMA Working Papers 2020-47, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    13. Aitazaz Ali Raja & Pierre Pinson & Jalal Kazempour & Sergio Grammatico, 2022. "A Market for Trading Forecasts: A Wagering Mechanism," Papers 2205.02668, arXiv.org, revised Oct 2022.
    14. Wang, Xiaoqian & Kang, Yanfei & Hyndman, Rob J. & Li, Feng, 2023. "Distributed ARIMA models for ultra-long time series," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1163-1184.
    15. Grzegorz Marcjasz & Micha{l} Narajewski & Rafa{l} Weron & Florian Ziel, 2022. "Distributional neural networks for electricity price forecasting," Papers 2207.02832, arXiv.org, revised Dec 2022.
    16. Bernhard Tröster & Ulrich Gunter, 2023. "The Financialization of Coffee, Cocoa and Cotton Value Chains: The Role of Physical Actors," Development and Change, International Institute of Social Studies, vol. 54(6), pages 1550-1574, November.
    17. Tetiana Zatonatska & Olena Liashenko & Yana Fareniuk & Oleksandr Dluhopolskyi & Artur Dmowski & Marzena Cichorzewska, 2022. "The Migration Influence on the Forecasting of Health Care Budget Expenditures in the Direction of Sustainability: Case of Ukraine," Sustainability, MDPI, vol. 14(21), pages 1-17, November.
    18. Wang, Shengjie & Kang, Yanfei & Petropoulos, Fotios, 2024. "Combining probabilistic forecasts of intermittent demand," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1038-1048.
    19. Raja, Aitazaz Ali & Pinson, Pierre & Kazempour, Jalal & Grammatico, Sergio, 2024. "A market for trading forecasts: A wagering mechanism," International Journal of Forecasting, Elsevier, vol. 40(1), pages 142-159.
    20. Jeroen Rombouts & Marie Ternes & Ines Wilms, 2024. "Cross-Temporal Forecast Reconciliation at Digital Platforms with Machine Learning," Papers 2402.09033, arXiv.org, revised May 2024.
    21. Jonathan Berrisch & Florian Ziel, 2022. "Distributional modeling and forecasting of natural gas prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1065-1086, September.
    22. Oscar Espinosa & Valeria Bejarano & Jeferson Ramos & Boris Martínez, 2023. "Statistical actuarial estimation of the Capitation Payment Unit from copula functions and deep learning: historical comparability analysis for the Colombian health system, 2015–2021," Health Economics Review, Springer, vol. 13(1), pages 1-20, December.
    23. Amjad Almusaed & Ibrahim Yitmen & Asaad Almssad, 2023. "Enhancing Smart Home Design with AI Models: A Case Study of Living Spaces Implementation Review," Energies, MDPI, vol. 16(6), pages 1-23, March.
    24. Niklas Valentin Lehmann, 2023. "Forecasting skill of a crowd-prediction platform: A comparison of exchange rate forecasts," Papers 2312.09081, arXiv.org.
    25. Jozef Barunik & Lubos Hanus, 2023. "Learning Probability Distributions of Day-Ahead Electricity Prices," Papers 2310.02867, arXiv.org, revised Oct 2023.
    26. Bergsteinsson, Hjörleifur G. & Sørensen, Mikkel Lindstrøm & Møller, Jan Kloppenborg & Madsen, Henrik, 2023. "Heat load forecasting using adaptive spatial hierarchies," Applied Energy, Elsevier, vol. 350(C).
    27. Silvia Golia & Luigi Grossi & Matteo Pelagatti, 2022. "Machine Learning Models and Intra-Daily Market Information for the Prediction of Italian Electricity Prices," Forecasting, MDPI, vol. 5(1), pages 1-21, December.
    28. Nghia Chu & Binh Dao & Nga Pham & Huy Nguyen & Hien Tran, 2022. "Predicting Mutual Funds' Performance using Deep Learning and Ensemble Techniques," Papers 2209.09649, arXiv.org, revised Jul 2023.
    29. Fałdziński, Marcin & Fiszeder, Piotr & Molnár, Peter, 2024. "Improving volatility forecasts: Evidence from range-based models," The North American Journal of Economics and Finance, Elsevier, vol. 69(PB).
    30. Takahashi, Carlos Kazunari & Figueiredo, Júlio César Bastos de & Scornavacca, Eusebio, 2024. "Investigating the diffusion of innovation: A comprehensive study of successive diffusion processes through analysis of search trends, patent records, and academic publications," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    31. Li, Xishu & Zuidwijk, Rob & de Koster, M.B.M, 2023. "Optimal competitive capacity strategies: Evidence from the container shipping market," Omega, Elsevier, vol. 115(C).
    32. Alroomi, Azzam & Karamatzanis, Georgios & Nikolopoulos, Konstantinos & Tilba, Anna & Xiao, Shujun, 2022. "Fathoming empirical forecasting competitions’ winners," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1519-1525.
    33. Qi, Lingzhi & Li, Xixi & Wang, Qiang & Jia, Suling, 2023. "fETSmcs: Feature-based ETS model component selection," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1303-1317.
    34. Richard Bean, 2023. "Forecasting the Monash Microgrid for the IEEE-CIS Technical Challenge," Energies, MDPI, vol. 16(3), pages 1-23, January.
    35. Guo, Su & Zheng, Kun & He, Yi & Kurban, Aynur, 2023. "The artificial intelligence-assisted short-term optimal scheduling of a cascade hydro-photovoltaic complementary system with hybrid time steps," Renewable Energy, Elsevier, vol. 202(C), pages 1169-1189.
    36. Rai, Amit & Shrivastava, Ashish & Jana, Kartick C., 2023. "Differential attention net: Multi-directed differential attention based hybrid deep learning model for solar power forecasting," Energy, Elsevier, vol. 263(PC).
    37. Swaminathan, Kritika & Venkitasubramony, Rakesh, 2024. "Demand forecasting for fashion products: A systematic review," International Journal of Forecasting, Elsevier, vol. 40(1), pages 247-267.
    38. Anita M. Bunea & Mariangela Guidolin & Piero Manfredi & Pompeo Della Posta, 2022. "Diffusion of Solar PV Energy in the UK: A Comparison of Sectoral Patterns," Forecasting, MDPI, vol. 4(2), pages 1-21, April.
    39. Andrea Savio & Luigi De Giovanni & Mariangela Guidolin, 2022. "Modelling Energy Transition in Germany: An Analysis through Ordinary Differential Equations and System Dynamics," Forecasting, MDPI, vol. 4(2), pages 1-18, April.
    40. Zheng, Zhuang & Shafique, Muhammad & Luo, Xiaowei & Wang, Shengwei, 2024. "A systematic review towards integrative energy management of smart grids and urban energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    41. Fernández, Joaquín Delgado & Menci, Sergio Potenciano & Lee, Chul Min & Rieger, Alexander & Fridgen, Gilbert, 2022. "Privacy-preserving federated learning for residential short-term load forecasting," Applied Energy, Elsevier, vol. 326(C).
    42. Emmanuel Senyo Fianu, 2022. "Analyzing and Forecasting Multi-Commodity Prices Using Variants of Mode Decomposition-Based Extreme Learning Machine Hybridization Approach," Forecasting, MDPI, vol. 4(2), pages 1-27, June.
    43. Ca’ Zorzi, Michele & Rubaszek, Michał, 2023. "How many fundamentals should we include in the behavioral equilibrium exchange rate model?," Economic Modelling, Elsevier, vol. 118(C).
    44. Radovan Šomplák & Veronika Smejkalová & Martin Rosecký & Lenka Szásziová & Vlastimír Nevrlý & Dušan Hrabec & Martin Pavlas, 2023. "Comprehensive Review on Waste Generation Modeling," Sustainability, MDPI, vol. 15(4), pages 1-29, February.
    45. Racek, Daniel & Thurner, Paul W. & Davidson, Brittany I. & Zhu, Xiao Xiang & Kauermann, Göran, 2024. "Conflict forecasting using remote sensing data: An application to the Syrian civil war," International Journal of Forecasting, Elsevier, vol. 40(1), pages 373-391.
    46. Huang, Congzhi & Yang, Mengyuan, 2023. "Memory long and short term time series network for ultra-short-term photovoltaic power forecasting," Energy, Elsevier, vol. 279(C).
    47. Fiszeder, Piotr & Fałdziński, Marcin & Molnár, Peter, 2023. "Modeling and forecasting dynamic conditional correlations with opening, high, low, and closing prices," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 308-321.
    48. Paul Ghelasi & Florian Ziel, 2023. "Hierarchical forecasting for aggregated curves with an application to day-ahead electricity price auctions," Papers 2305.16255, arXiv.org.
    49. Katarzyna Maciejowska & Bartosz Uniejewski & Rafa{l} Weron, 2022. "Forecasting Electricity Prices," Papers 2204.11735, arXiv.org.

  4. Michael P. Clements, 2020. "Individual Forecaster Perceptions of the Persistence of Shocks to GDP," ICMA Centre Discussion Papers in Finance icma-dp2020-02, Henley Business School, University of Reading.

    Cited by:

    1. Dovern, Jonas & Glas, Alexander & Kenny, Geoff, 2023. "Testing for differences in survey-based density expectations: a compositional data approach," Working Paper Series 2791, European Central Bank.

  5. 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.

    Cited by:

    1. Knüppel, Malte & Krüger, Fabian, 2019. "Forecast uncertainty, disagreement, and the linear pool," Discussion Papers 28/2019, Deutsche Bundesbank.
    2. Koop, Gary & McIntyre, Stuart & Mitchell, James & Poon, Aubrey, 2020. "Reconciled Estimates of Monthly GDP in the US," EMF Research Papers 37, Economic Modelling and Forecasting Group.

  6. Michael P. Clements, 2020. "Do Survey Joiners and Leavers Differ from Regular Participants? The US SPF GDP Growth and Inflation Forecasts," ICMA Centre Discussion Papers in Finance icma-dp2020-01, Henley Business School, University of Reading.

    Cited by:

    1. Klein, Tony, 2021. "Agree to Disagree? Predictions of U.S. Nonfarm Payroll Changes between 2008 and 2020 and the Impact of the COVID19 Labor Shock," QBS Working Paper Series 2021/07, Queen's University Belfast, Queen's Business School.
    2. Toshitaka Sekine & Frank Packer & Shunichi Yoneyama, 2022. "Individual Trend Inflation," Working Papers on Central Bank Communication 042, University of Tokyo, Graduate School of Economics.
    3. Michael P. Clements, 2020. "Are Some Forecasters’ Probability Assessments of Macro Variables Better Than Those of Others?," Econometrics, MDPI, vol. 8(2), pages 1-16, May.
    4. Klein, Tony, 2022. "Agree to disagree? Predictions of U.S. nonfarm payroll changes between 2008 and 2020 and the impact of the COVID19 labor shock," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 264-286.
    5. Anis Ochi & Yosra Saidi & Mohamed Ali Labidi, 2023. "Non-linear Threshold Effect of Governance Quality on Economic Growth in African Countries: Evidence from Panel Smooth Transition Regression Approach," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 14(4), pages 4707-4729, December.

  7. Clements, Michael P. & Galvao, Ana Beatriz, 2019. "Measuring the Effects of Expectations Shocks," EMF Research Papers 31, Economic Modelling and Forecasting Group.

    Cited by:

    1. Christoph Gortz & Christopher Gunn & Thomas A. Lubik, 2019. "What Drives Inventory Accumulation? News on Rates of Return and Marginal Costs," Discussion Papers 19-09, Department of Economics, University of Birmingham.
    2. Klein, Tony, 2021. "Agree to Disagree? Predictions of U.S. Nonfarm Payroll Changes between 2008 and 2020 and the Impact of the COVID19 Labor Shock," QBS Working Paper Series 2021/07, Queen's University Belfast, Queen's Business School.
    3. Ahmed, M. Iqbal & Cassou, Steven P., 2021. "Asymmetries in the effects of unemployment expectation shocks as monetary policy shifts with economic conditions," Economic Modelling, Elsevier, vol. 100(C).
    4. Mr. Philip Barrett & Jonathan J. Adams, 2022. "Shocks to Inflation Expectations," IMF Working Papers 2022/072, International Monetary Fund.
    5. An, Zidong & Sheng, Xuguang Simon & Zheng, Xinye, 2023. "What is the role of perceived oil price shocks in inflation expectations?," Energy Economics, Elsevier, vol. 126(C).
    6. Danilo Cascaldi-Garcia, 2022. "Forecast Revisions as Instruments for News Shocks," International Finance Discussion Papers 1341, Board of Governors of the Federal Reserve System (U.S.).
    7. Michael P. Clements, 2020. "Do Survey Joiners and Leavers Differ from Regular Participants? The US SPF GDP Growth and Inflation Forecasts," ICMA Centre Discussion Papers in Finance icma-dp2020-01, Henley Business School, University of Reading.
    8. Ma, Xiaohan & Samaniego, Roberto, 2022. "Business cycle dynamics when neutral and investment-specific technology shocks are imperfectly observable," Journal of Mathematical Economics, Elsevier, vol. 101(C).
    9. Klein, Tony, 2022. "Agree to disagree? Predictions of U.S. nonfarm payroll changes between 2008 and 2020 and the impact of the COVID19 labor shock," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 264-286.
    10. Lin, Jilei & Eck, Daniel J., 2021. "Minimizing post-shock forecasting error through aggregation of outside information," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1710-1727.

  8. Michael Clements, 2017. "Do forecasters target first or later releases of national accounts data?," ICMA Centre Discussion Papers in Finance icma-dp2017-03, Henley Business School, University of Reading.

    Cited by:

    1. Franses, Ph.H.B.F. & Welz, M., 2018. "Evaluating heterogeneous forecasts for vintages of macroeconomic variables," Econometric Institute Research Papers EI2018-47, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Sinclair, Tara M., 2019. "Characteristics and implications of Chinese macroeconomic data revisions," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1108-1117.
    3. Michael Pfarrhofer, 2024. "Forecasts with Bayesian vector autoregressions under real time conditions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 771-801, April.
    4. Borup, Daniel & Schütte, Erik Christian Montes, 2022. "Asset pricing with data revisions," Journal of Financial Markets, Elsevier, vol. 59(PB).
    5. Tim Köhler & Jörg Döpke, 2023. "Will the last be the first? Ranking German macroeconomic forecasters based on different criteria," Empirical Economics, Springer, vol. 64(2), pages 797-832, February.
    6. Feng, Qianqian & Sun, Xiaolei & Hao, Jun & Li, Jianping, 2021. "Predictability dynamics of multifactor-influenced installed capacity: A perspective of country clustering," Energy, Elsevier, vol. 214(C).
    7. Meade, Nigel & Driver, Ciaran, 2023. "Differing behaviours of forecasters of UK GDP growth," International Journal of Forecasting, Elsevier, vol. 39(2), pages 772-790.
    8. Giovannelli, Alessandro & Pericoli, Filippo Maria, 2020. "Are GDP forecasts optimal? Evidence on European countries," International Journal of Forecasting, Elsevier, vol. 36(3), pages 963-973.

  9. Michael P Clements & Ana Beatriz Galvao, 2017. "Data Revisions and Real-time Probabilistic Forecasting of Macroeconomic Variables," ICMA Centre Discussion Papers in Finance icma-dp2017-01, Henley Business School, University of Reading.

    Cited by:

    1. Ana Beatriz Galvão & Marta Lopresto, 2020. "Real-time Probabilistic Nowcasts of UK Quarterly GDP Growth using a Mixed-Frequency Bottom-up Approach," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2020-06, Economic Statistics Centre of Excellence (ESCoE).
    2. 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.

  10. Michael Clements, 2016. "Are Macroeconomic Density Forecasts Informative?," ICMA Centre Discussion Papers in Finance icma-dp2016-02, Henley Business School, University of Reading.

    Cited by:

    1. Gergely Ganics & Barbara Rossi & Tatevik Sekhposyan, 2019. "From fixed-event to fixed-horizon density forecasts: Obtaining measures of multi-horizon uncertainty from survey density forecasts," Economics Working Papers 1689, Department of Economics and Business, Universitat Pompeu Fabra.
    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. Michael P. Clements, 2022. "Individual forecaster perceptions of the persistence of shocks to GDP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 640-656, April.
    4. Knüppel, Malte & Schultefrankenfeld, Guido, 2019. "Assessing the uncertainty in central banks’ inflation outlooks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1748-1769.
    5. 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.
    6. Alexander Glas & Matthias Hartmann, 2022. "Uncertainty measures from partially rounded probabilistic forecast surveys," Quantitative Economics, Econometric Society, vol. 13(3), pages 979-1022, July.
    7. Ana Beatriz Galvão & James Mitchell, 2019. "Measuring Data Uncertainty: An Application using the Bank of England's "Fan Charts" for Historical GDP Growth," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2019-08, Economic Statistics Centre of Excellence (ESCoE).
    8. Rossi, Barbara & Ganics, Gergely & Sekhposyan, Tatevik, 2020. "From Fixed-event to Fixed-horizon Density Forecasts: Obtaining Measures of Multi-horizon Uncertainty from Survey Density Foreca," CEPR Discussion Papers 14267, C.E.P.R. Discussion Papers.
    9. Todd E. Clark & Michael W. McCracken & Elmar Mertens, 2017. "Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors," Working Papers (Old Series) 1715, Federal Reserve Bank of Cleveland.
    10. Ana Beatriz Galvão & James Mitchell, 2023. "Real‐Time Perceptions of Historical GDP Data Uncertainty," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 457-481, June.
    11. Knüppel, Malte & Krüger, Fabian, 2019. "Forecast uncertainty, disagreement, and the linear pool," Discussion Papers 28/2019, Deutsche Bundesbank.
    12. Galvao, Ana Beatriz & Garratt, Anthony & Mitchell, James, 2020. "Does Judgment Improve Macroeconomic Density Forecasts?," EMF Research Papers 33, Economic Modelling and Forecasting Group.
    13. 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.
    14. Todd E. Clark & Gergely Ganics & Elmar Mertens, 2022. "What is the Predictive Value of SPF Point and Density Forecasts?," Working Papers 22-37, Federal Reserve Bank of Cleveland.
    15. Michael P. Clements, 2020. "Do Survey Joiners and Leavers Differ from Regular Participants? The US SPF GDP Growth and Inflation Forecasts," ICMA Centre Discussion Papers in Finance icma-dp2020-01, Henley Business School, University of Reading.
    16. Todd E. Clark & Gergely Ganics & Elmar Mertens, 2022. "Constructing Fan Charts from the Ragged Edge of SPF Forecasts," Working Papers 22-36, Federal Reserve Bank of Cleveland.
    17. Michael P. Clements, 2020. "Are Some Forecasters’ Probability Assessments of Macro Variables Better Than Those of Others?," Econometrics, MDPI, vol. 8(2), pages 1-16, May.
    18. Barbara Rossi & Tatevik Sekhposyan, 2017. "Macroeconomic uncertainty indices for the Euro Area and its individual member countries," Empirical Economics, Springer, vol. 53(1), pages 41-62, August.
    19. Fabian Kruger & Hendrik Plett, 2022. "Prediction intervals for economic fixed-event forecasts," Papers 2210.13562, arXiv.org, revised Mar 2024.
    20. Conrad, Christian & Glas, Alexander, 2018. "‘Déjà vol’ revisited: Survey forecasts of macroeconomic variables predict volatility in the cross-section of industry portfolios," Working Papers 0655, University of Heidelberg, Department of Economics.
    21. Tsuchiya, Yoichi, 2022. "Evaluating the European Central Bank’s uncertainty forecasts," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 321-330.
    22. Tatjana Dahlhaus & Tatevik Sekhposyan, 2018. "Monetary Policy Uncertainty: A Tale of Two Tails," Staff Working Papers 18-50, Bank of Canada.

  11. Jennifer Castle & David Hendry & Michael P. Clements, 2016. "An Overview of Forecasting Facing Breaks," Economics Series Working Papers 779, University of Oxford, Department of Economics.

    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. Jennifer L. Castle & David F. Hendry & Andrew B. Martinez, 2022. "The Historical Role of Energy in UK Inflation and Productivity and Implications for Price Inflation in 2022," Working Papers 2022-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    3. Döpke, Jörg & Fritsche, Ulrich & Müller, Karsten, 2019. "Has macroeconomic forecasting changed after the Great Recession? Panel-based evidence on forecast accuracy and forecaster behavior from Germany," Journal of Macroeconomics, Elsevier, vol. 62(C).
    4. Jeronymo Marcondes Pinto & Jennifer L. Castle, 2022. "Machine Learning Dynamic Switching Approach to Forecasting in the Presence of Structural Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(2), pages 129-157, July.
    5. Marta Boczon, 2018. "Balanced Growth Approach to Forecasting Recessions," Working Paper 6487, Department of Economics, University of Pittsburgh.
    6. Luca Nocciola, "undated". "Finite sample forecast properties and window length under breaks in cointegrated systems," Discussion Papers 19/07, University of Nottingham, Granger Centre for Time Series Econometrics.
    7. Tebecis, Talis, 2023. "Have climate policies been effective in Austria? A reverse causal analysis," Department of Economics Working Paper Series 346, WU Vienna University of Economics and Business.
    8. Jörg Döpke & Ulrich Fritsche & Karsten Müller, 2018. "Has Macroeconomic Forecasting changed after the Great Recession? - Panel-based Evidence on Accuracy and Forecaster Behaviour from Germany," Macroeconomics and Finance Series 201803, University of Hamburg, Department of Socioeconomics.
    9. Marta Boczoń & Jean-François Richard, 2020. "Balanced Growth Approach to Tracking Recessions," Econometrics, MDPI, vol. 8(2), pages 1-35, April.
    10. Muhammad Jahanzeb Malik & Muhammad Nadim Hanif, 2019. "Learning from Errors While Forecasting Inflation: A Case for Intercept Correction," International Econometric Review (IER), Econometric Research Association, vol. 11(1), pages 24-38, April.
    11. William D. Larson & Tara M. Sinclair, 2020. "Nowcasting unemployment insurance claims in the time of COVID-19," CAMA Working Papers 2020-63, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    12. Fokin, Nikita, 2021. "The importance of modeling structural breaks in forecasting Russian GDP," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 63, pages 5-29.
    13. Stephen McKnight & Alexander Mihailov & Fabio Rumler, 2018. "NKPC-Based Inflation Forecasts with a Time-Varying Trend," Serie documentos de trabajo del Centro de Estudios Económicos 2018-05, El Colegio de México, Centro de Estudios Económicos.
    14. Xin, Daleng & Ahmad, Manzoor & Lei, Hong & Khattak, Shoukat Iqbal, 2021. "Do innovation in environmental-related technologies asymmetrically affect carbon dioxide emissions in the United States?," Technology in Society, Elsevier, vol. 67(C).
    15. Talis Tebecis, 2023. "Have climate policies been effective in Austria? A reverse causal analysis," Department of Economics Working Papers wuwp346, Vienna University of Economics and Business, Department of Economics.
    16. Jeronymo Marcondes Pinto & Emerson Fernandes Marçal, 2023. "An artificial intelligence approach to forecasting when there are structural breaks: a reinforcement learning-based framework for fast switching," Empirical Economics, Springer, vol. 65(4), pages 1729-1759, October.
    17. Castle, Jennifer L. & Hendry, David F. & Martinez, Andrew B., 2023. "The historical role of energy in UK inflation and productivity with implications for price inflation," Energy Economics, Elsevier, vol. 126(C).
    18. igescu, iulia, 2020. "Describing Location Shifts with One Class Support Vector Machines," MPRA Paper 100984, University Library of Munich, Germany.

  12. Michael P. Clements, 2015. "Assessing Macro Uncertainty In Real-Time When Data Are Subject To Revision," ICMA Centre Discussion Papers in Finance icma-dp2015-02, Henley Business School, University of Reading.

    Cited by:

    1. Ana Beatriz Galvão & Marta Lopresto, 2020. "Real-time Probabilistic Nowcasts of UK Quarterly GDP Growth using a Mixed-Frequency Bottom-up Approach," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2020-06, Economic Statistics Centre of Excellence (ESCoE).
    2. Michael P Clements & Ana Beatriz Galvao, 2017. "Data Revisions and Real-time Probabilistic Forecasting of Macroeconomic Variables," ICMA Centre Discussion Papers in Finance icma-dp2017-01, Henley Business School, University of Reading.
    3. Graziano Moramarco, 2022. "Measuring Global Macroeconomic Uncertainty and Cross-Country Uncertainty Spillovers," Econometrics, MDPI, vol. 11(1), pages 1-29, December.
    4. 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.
    5. Galvao, Ana Beatriz, 2016. "Data Revisions and DSGE Models," EMF Research Papers 11, Economic Modelling and Forecasting Group.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Graziano Moramarco, 2020. "Measuring Global Macroeconomic Uncertainty," Working Papers wp1148, Dipartimento Scienze Economiche, Universita' di Bologna.
    12. Baetje, Fabian & Friedrici, Karola, 2016. "Does cross-sectional forecast dispersion proxy for macroeconomic uncertainty? New empirical evidence," Economics Letters, Elsevier, vol. 143(C), pages 38-43.

  13. Michael P. Clements & Ana Beatriz Galvão, 2014. "Measuring Macroeconomic Uncertainty: US Inflation and Output Growth," ICMA Centre Discussion Papers in Finance icma-dp2014-04, Henley Business School, University of Reading.

    Cited by:

    1. 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.
    2. Pierre L. Siklos, 2017. "What has publishing inflation forecasts accomplished? Central banks and their competitors," CAMA Working Papers 2017-33, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    3. Monique Reid & Pierre Siklos, 2024. "Firm Level Expectations and Macroeconomic Conditions: Underpinnings and Disagreement," CAMA Working Papers 2024-05, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    4. Hartmann, Matthias & Herwartz, Helmut & Ulm, Maren, 2017. "A comparative assessment of alternative ex ante measures of inflation uncertainty," International Journal of Forecasting, Elsevier, vol. 33(1), pages 76-89.
    5. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2014. "Density forecasts with MIDAS models," Working Paper 2014/10, Norges Bank.
    6. Pierre L. Siklos, 2016. "Forecast Disagreement and the Inflation Outlook: New International Evidence," IMES Discussion Paper Series 16-E-03, Institute for Monetary and Economic Studies, Bank of Japan.
    7. Charemza, Wojciech & Díaz, Carlos & Makarova, Svetlana, 2019. "Quasi ex-ante inflation forecast uncertainty," International Journal of Forecasting, Elsevier, vol. 35(3), pages 994-1007.
    8. Carmen PINTILESCU & Mircea ASANDULUI & Elena-Daniela VIORICA & Danut-Vasile JEMNA, 2016. "Investigation On The Causal Relationship Between Inflation, Output Growth And Their Uncertainties In Romania," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 17, pages 71-89, June.
    9. Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2019. "Density Forecasting," BEMPS - Bozen Economics & Management Paper Series BEMPS59, Faculty of Economics and Management at the Free University of Bozen.

  14. Michael P. Clements, 2014. "Do US Macroeconomic Forecasters Exaggerate Their Differences?," ICMA Centre Discussion Papers in Finance icma-dp2014-10, Henley Business School, University of Reading.

    Cited by:

    1. Cole, Stephen J. & Milani, Fabio, 2021. "Heterogeneity in individual expectations, sentiment, and constant-gain learning," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 627-650.
    2. Schultefrankenfeld, Guido, 2017. "Appropriate monetary policy and forecast disagreement at the FOMC," Discussion Papers 39/2017, Deutsche Bundesbank.
    3. Michael P. Clements, 2018. "Do Macroforecasters Herd?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(2-3), pages 265-292, March.
    4. Monique Reid & Pierre Siklos, 2024. "Firm Level Expectations and Macroeconomic Conditions: Underpinnings and Disagreement," CAMA Working Papers 2024-05, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    5. Meade, Nigel & Driver, Ciaran, 2023. "Differing behaviours of forecasters of UK GDP growth," International Journal of Forecasting, Elsevier, vol. 39(2), pages 772-790.
    6. Yanwei Jia & Jussi Keppo & Ville Satopää, 2023. "Herding in Probabilistic Forecasts," Management Science, INFORMS, vol. 69(5), pages 2713-2732, May.
    7. Goldstein, Nathan & Zilberfarb, Ben-Zion, 2021. "Do forecasters really care about consensus?," Economic Modelling, Elsevier, vol. 100(C).

  15. Jennifer Castle & David Hendry & Michael P. Clements, 2014. "Robust Approaches to Forecasting," Economics Series Working Papers 697, University of Oxford, Department of Economics.

    Cited by:

    1. Michael Clements, 2016. "Are Macroeconomic Density Forecasts Informative?," ICMA Centre Discussion Papers in Finance icma-dp2016-02, Henley Business School, University of Reading.
    2. Pinto, Jeronymo Marcondes & Marçal, Emerson Fernandes, 2019. "Cross-validation based forecasting method: a machine learning approach," Textos para discussão 498, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    3. Jeronymo Marcondes Pinto & Jennifer L. Castle, 2022. "Machine Learning Dynamic Switching Approach to Forecasting in the Presence of Structural Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(2), pages 129-157, July.
    4. Neil R. Ericsson, 2017. "How Biased Are U.S. Government Forecasts of the Federal Debt?," International Finance Discussion Papers 1189, Board of Governors of the Federal Reserve System (U.S.).
    5. David Hendry & John Muellbauer, 2017. "The future of macroeconomics: Macro theory and models at the Bank of England," Economics Series Working Papers 832, University of Oxford, Department of Economics.
    6. Neil R. Ericsson, 2015. "Eliciting GDP Forecasts from the FOMC’s Minutes Around the Financial Crisis," International Finance Discussion Papers 1152, Board of Governors of the Federal Reserve System (U.S.).
    7. 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.
    8. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Selecting a Model for Forecasting," Econometrics, MDPI, vol. 9(3), pages 1-35, June.
    9. Hendry, David F., 2018. "Deciding between alternative approaches in macroeconomics," International Journal of Forecasting, Elsevier, vol. 34(1), pages 119-135.
    10. Emerson Fernandes Marçal & Eli Hadad Junior, 2016. "Is It Possible to Beat the Random Walk Model in Exchange Rate Forecasting? More Evidence for Brazilian Case," Brazilian Review of Finance, Brazilian Society of Finance, vol. 14(1), pages 65-88.
    11. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2020. "Short-term forecasting of the Coronavirus Pandemic - 2020-04-27," Economics Papers 2020-W06, Economics Group, Nuffield College, University of Oxford.
    12. David F. Hendry, 2020. "A Short History of Macro-econometric Modelling," Economics Papers 2020-W01, Economics Group, Nuffield College, University of Oxford.
    13. Neil R. Ericsson, 2021. "Dynamic Econometrics in Action: A Biography of David F. Hendry," International Finance Discussion Papers 1311, Board of Governors of the Federal Reserve System (U.S.).
    14. Jennifer Castle & Takamitsu Kurita, 2019. "Modelling and forecasting the dollar-pound exchange rate in the presence of structural breaks," Economics Series Working Papers 866, University of Oxford, Department of Economics.
    15. William D. Larson & Tara M. Sinclair, 2020. "Nowcasting unemployment insurance claims in the time of COVID-19," CAMA Working Papers 2020-63, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    16. Clements, Michael P., 2016. "Real-time factor model forecasting and the effects of instability," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 661-675.
    17. 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.
    18. Andrew B. Martinez & Jennifer L. Castle & David F. Hendry, 2021. "Smooth Robust Multi-Horizon Forecasts," Economics Papers 2021-W01, Economics Group, Nuffield College, University of Oxford.
    19. Castle, Jennifer L. & Kurita, Takamitsu, 2021. "A dynamic econometric analysis of the dollar-pound exchange rate in an era of structural breaks and policy regime shifts," Journal of Economic Dynamics and Control, Elsevier, vol. 128(C).
    20. John B. Guerard, 2024. "Sir David Hendry: An Appreciation from Wall Street and What Macroeconomics Got Right," Working Papers 2024-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting, revised Feb 2024.
    21. Spiliotis, Evangelos & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2019. "Tales from tails: On the empirical distributions of forecasting errors and their implication to risk," International Journal of Forecasting, Elsevier, vol. 35(2), pages 687-698.
    22. Doornik, Jurgen A. & Castle, Jennifer L. & Hendry, David F., 2022. "Short-term forecasting of the coronavirus pandemic," International Journal of Forecasting, Elsevier, vol. 38(2), pages 453-466.
    23. Papailias, Fotis & Thomakos, Dimitrios, 2017. "EXSSA: SSA-based reconstruction of time series via exponential smoothing of covariance eigenvalues," International Journal of Forecasting, Elsevier, vol. 33(1), pages 214-229.
    24. Jeronymo Marcondes Pinto & Emerson Fernandes Marçal, 2023. "An artificial intelligence approach to forecasting when there are structural breaks: a reinforcement learning-based framework for fast switching," Empirical Economics, Springer, vol. 65(4), pages 1729-1759, October.
    25. Kyriazi, Foteini & Thomakos, Dimitrios D. & Guerard, John B., 2019. "Adaptive learning forecasting, with applications in forecasting agricultural prices," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1356-1369.
    26. Lin, Jilei & Eck, Daniel J., 2021. "Minimizing post-shock forecasting error through aggregation of outside information," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1710-1727.

  16. Michael P. Clements, 2014. "Long-Run Restrictions and Survey Forecasts of Output, Consumption and Investment," ICMA Centre Discussion Papers in Finance icma-dp2014-02, Henley Business School, University of Reading.

    Cited by:

    1. Kapetanios, George & Millard, Stephen & Petrova, Katerina & Price, Simon, 2019. "Time-varying cointegration and the UK great ratios," Bank of England working papers 789, Bank of England.
    2. Michael Clements, 2016. "Are Macro-Forecasters Essentially The Same? An Analysis of Disagreement, Accuracy and Efficiency," ICMA Centre Discussion Papers in Finance icma-dp2016-08, Henley Business School, University of Reading.
    3. Kapetanios, George & Millard, Stephen & Petrova, Katerina & Price, Simon, 2020. "Time-varying cointegration with an application to the UK Great Ratios," Economics Letters, Elsevier, vol. 193(C).
    4. Zhao, Yongchen, 2024. "Uncertainty of household inflation expectations: Reconciling point and density forecasts," Economics Letters, Elsevier, vol. 234(C).

  17. Michael P. Clements, 2014. "Real-Time Factor Model Forecasting and the Effects of Instability," ICMA Centre Discussion Papers in Finance icma-dp2014-05, Henley Business School, University of Reading.

    Cited by:

    1. Hännikäinen, Jari, 2017. "When does the yield curve contain predictive power? Evidence from a data-rich environment," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1044-1064.
    2. Kuusela, Annika & Hännikäinen, Jari, 2017. "What do the shadow rates tell us about future inflation?," MPRA Paper 80542, University Library of Munich, Germany.
    3. Bragoli, Daniela, 2017. "Now-casting the Japanese economy," International Journal of Forecasting, Elsevier, vol. 33(2), pages 390-402.
    4. 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.
    5. Dominik Bertsche & Ralf Brüggemann & Christian Kascha, 2018. "Directed Graphs and Variable Selection in Large Vector Autoregressive Models," Working Paper Series of the Department of Economics, University of Konstanz 2018-08, Department of Economics, University of Konstanz.

  18. Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.

    Cited by:

    1. 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.
    2. 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.
    3. Neil R. Ericsson & Mohammed H. I. Dore & Hassan Butt, 2022. "Detecting and Quantifying Structural Breaks in Climate," Econometrics, MDPI, vol. 10(4), pages 1-27, November.
    4. Valentina Corradi & Norman Swanson, 2013. "Testing for Structural Stability of Factor Augmented Forecasting Models," Departmental Working Papers 201314, Rutgers University, Department of Economics.

  19. Clements, Michael P., 2012. "US inflation expectations and heterogeneous loss functions, 1968–2010," Economic Research Papers 270653, University of Warwick - Department of Economics.

    Cited by:

    1. Zhao, Yongchen, 2023. "Internal consistency of household inflation expectations: Point forecasts vs. density forecasts," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1713-1735.
    2. Galvao, Ana Beatriz & Garratt, Anthony & Mitchell, James, 2020. "Does Judgment Improve Macroeconomic Density Forecasts?," EMF Research Papers 33, Economic Modelling and Forecasting Group.
    3. 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.
    4. Maurizio Bovi & Roy Cerqueti, 2016. "Forecasting macroeconomic fundamentals in economic crises," Annals of Operations Research, Springer, vol. 247(2), pages 451-469, December.
    5. Conrad, Christian, 2017. "When does information on forecast variance improve the performance of a combined forecast?," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168200, Verein für Socialpolitik / German Economic Association.

  20. Clements, Michael P., 2012. "Probability Distributions or Point Predictions? Survey Forecasts of US Output Growth and Inflation," Economic Research Papers 270748, University of Warwick - Department of Economics.

    Cited by:

    1. Gergely Ganics & Barbara Rossi & Tatevik Sekhposyan, 2019. "From fixed-event to fixed-horizon density forecasts: Obtaining measures of multi-horizon uncertainty from survey density forecasts," Economics Working Papers 1689, Department of Economics and Business, Universitat Pompeu Fabra.
    2. Olesya Grishchenko & Sarah Mouabbi & Jean‐Paul Renne, 2019. "Measuring Inflation Anchoring and Uncertainty: A U.S. and Euro Area Comparison," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 51(5), pages 1053-1096, August.
    3. Michael Clements, 2016. "Are Macroeconomic Density Forecasts Informative?," ICMA Centre Discussion Papers in Finance icma-dp2016-02, Henley Business School, University of Reading.
    4. Glas, Alexander, 2020. "Five dimensions of the uncertainty–disagreement linkage," International Journal of Forecasting, Elsevier, vol. 36(2), pages 607-627.
    5. Robert Rich & Joseph Tracy, 2021. "A Closer Look at the Behavior of Uncertainty and Disagreement: Micro Evidence from the Euro Area," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(1), pages 233-253, February.
    6. Fernando Borraz & Laura Zacheo, 2018. "Inattention, Disagreement and Internal (In)Consistency of Inflation Forecasts," Documentos de trabajo 2018007, Banco Central del Uruguay.
    7. Alexander Dietrich & Edward S. Knotek & Kristian Ove R. Myrseth & Robert W. Rich & Raphael Schoenle & Michael Weber, 2022. "Greater Than the Sum of the Parts: Aggregate vs. Aggregated Inflation Expectations," Working Papers 22-20, Federal Reserve Bank of Cleveland.
    8. Rossi, Barbara & Ganics, Gergely & Sekhposyan, Tatevik, 2020. "From Fixed-event to Fixed-horizon Density Forecasts: Obtaining Measures of Multi-horizon Uncertainty from Survey Density Foreca," CEPR Discussion Papers 14267, C.E.P.R. Discussion Papers.
    9. Zhao, Yongchen, 2023. "Internal consistency of household inflation expectations: Point forecasts vs. density forecasts," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1713-1735.
    10. Stanisławska, Ewa & Paloviita, Maritta & Łyziak, Tomasz, 2019. "Assessing reliability of aggregated inflation views in the European Commission consumer survey," Bank of Finland Research Discussion Papers 10/2019, Bank of Finland.
    11. Dietrich, Alexander M., 2023. "Consumption categories, household attention, and inflation expectations: Implications for optimal monetary policy," University of Tübingen Working Papers in Business and Economics 157, University of Tuebingen, Faculty of Economics and Social Sciences, School of Business and Economics.
    12. Michael P. Clements, 2015. "Are Professional Macroeconomic Forecasters Able To Do Better Than Forecasting Trends?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(2-3), pages 349-382, March.
    13. Glas, Alexander & Hartmann, Matthias, 2016. "Inflation uncertainty, disagreement and monetary policy: Evidence from the ECB Survey of Professional Forecasters," Journal of Empirical Finance, Elsevier, vol. 39(PB), pages 215-228.
    14. 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.
    15. Sheng, Xuguang (Simon) & Thevenot, Maya, 2015. "Quantifying differential interpretation of public information using financial analysts’ earnings forecasts," International Journal of Forecasting, Elsevier, vol. 31(2), pages 515-530.
    16. Todd E. Clark & Gergely Ganics & Elmar Mertens, 2022. "What is the Predictive Value of SPF Point and Density Forecasts?," Working Papers 22-37, Federal Reserve Bank of Cleveland.

  21. Clements, Michael P., 2012. "Subjective and Ex Post Forecast Uncertainty: US Inflation and Output Growth," Economic Research Papers 270629, University of Warwick - Department of Economics.

    Cited by:

    1. Katharina Glass & Ulrich Fritsche, 2015. "Real-time Macroeconomic Data and Uncertainty," Macroeconomics and Finance Series 201406, University of Hamburg, Department of Socioeconomics.

  22. Michael P. Clements & Ana Beatriz Galvão, 2011. "Improving Real-time Estimates of Output Gaps and Inflation Trends with Multiple-vintage Models," Working Papers 678, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. Martin Slanicay & Jan Čapek & Miroslav Hloušek, 2016. "Some Notes On Problematic Issues In Dsge Models," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 61(210), pages 79-100, July - Se.
    2. 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.
    3. Clements, Michael P. & Galvão, Ana Beatriz, 2013. "Forecasting with vector autoregressive models of data vintages: US output growth and inflation," International Journal of Forecasting, Elsevier, vol. 29(4), pages 698-714.
    4. Capek Jan, 2015. "Estimating DSGE model parameters in a small open economy: Do real-time data matter?," Review of Economic Perspectives, Sciendo, vol. 15(1), pages 89-114, March.

  23. Clements, Michael P., 2011. "Do Professional Forecasters Pay Attention to Data Releases?," Economic Research Papers 270768, University of Warwick - Department of Economics.

    Cited by:

    1. Keith Sill, 2014. "Forecast disagreement in the Survey of Professional Forecasters," Business Review, Federal Reserve Bank of Philadelphia, issue Q2, pages 15-24.
    2. Pedersen, Michael, 2015. "What affects the predictions of private forecasters? The role of central bank forecasts in Chile," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1043-1055.
    3. de Mendonça, Helder Ferreira & Vereda, Luciano & Araujo, Mateus de Azevedo, 2022. "What type of information calls the attention of forecasters? Evidence from survey data in an emerging market," Journal of International Money and Finance, Elsevier, vol. 129(C).
    4. Katharina Glass & Ulrich Fritsche, 2015. "Real-time Macroeconomic Data and Uncertainty," Macroeconomics and Finance Series 201406, University of Hamburg, Department of Socioeconomics.
    5. Alex Ilek, 2020. "Are monetary surprises effective? The view of professional forecasters in Israel," Bank of Israel Working Papers 2020.09, Bank of Israel.
    6. Nikos Apokoritis & Gabriele Galati & Richhild Moessner & Federica Teppa, 2019. "Inflation expectations anchoring: new insights from micro evidence of a survey at high-frequency and of distributions," BIS Working Papers 809, Bank for International Settlements.
    7. Meade, Nigel & Driver, Ciaran, 2023. "Differing behaviours of forecasters of UK GDP growth," International Journal of Forecasting, Elsevier, vol. 39(2), pages 772-790.
    8. Travis J. Berge & Andrew C. Chang & Nitish R. Sinha, 2019. "Evaluating the Conditionality of Judgmental Forecasts," Finance and Economics Discussion Series 2019-002, Board of Governors of the Federal Reserve System (U.S.).
    9. Ullrich Heilemann & Karsten Müller, 2018. "Wenig Unterschiede – Zur Treffsicherheit Internationaler Prognosen und Prognostiker [Few differences—on the accuracy of international forecasts and forecaster]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 12(3), pages 195-233, December.
    10. Heilemann Ullrich & Schnorr-Bäcker Susanne, 2017. "Could the start of the German recession 2008–2009 have been foreseen? Evidence from Real-Time Data," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 237(1), pages 29-62, February.
    11. Hossein Hassani & Jan Coreman & Saeed Heravi & Joshy Easaw, 2018. "Forecasting Inflation Rate: Professional Against Academic, Which One is More Accurate," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 16(3), pages 631-646, September.

  24. Clements, Michael P. & Beatriz Galvao, Ana, 2010. "Real-time Forecasting of Inflation and Output Growth in the Presence of Data Revisions," Economic Research Papers 270771, University of Warwick - Department of Economics.

    Cited by:

    1. Carlos A. Medel, 2013. "How informative are in-sample information criteria to forecasting? The case of Chilean GDP," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 50(1), pages 133-161, May.
    2. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
    3. Christiane Baumeister & Lutz Kilian, 2011. "Real-Time Forecasts of the Real Price of Oil," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 326-336, September.
    4. Michael P. Clements & Ana Beatriz Galvão, 2011. "Improving Real-time Estimates of Output Gaps and Inflation Trends with Multiple-vintage Models," Working Papers 678, Queen Mary University of London, School of Economics and Finance.
    5. 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.
    6. Clements Michael P., 2012. "Forecasting U.S. Output Growth with Non-Linear Models in the Presence of Data Uncertainty," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(1), pages 1-27, January.
    7. Cecilia Frale & Valentina Raponi, 2011. "Revisions in ocial data and forecasting," Working Papers LuissLab 1194, Dipartimento di Economia e Finanza, LUISS Guido Carli.

  25. David Hendry & Michael P. Clements, 2010. "Forecasting from Mis-specified Models in the Presence of Unanticipated Location Shifts," Economics Series Working Papers 484, University of Oxford, Department of Economics.

    Cited by:

    1. Mihaela Bratu, 2011. "The Assessement Of Uncertainty In Predictions Determined By The Variables Aggregation," Annales Universitatis Apulensis Series Oeconomica, Faculty of Sciences, "1 Decembrie 1918" University, Alba Iulia, vol. 2(13), pages 1-31.
    2. Bratu Simionescu Mihaela, 2012. "Variables Aggregation-Source of Uncertainty in Forecasting," Scientific Annals of Economics and Business, Sciendo, vol. 59(2), pages 1-13, December.

  26. Clements, Michael P. & Beatriz Galvao, Ana, 2008. "First Announcements and Real Economic Activity," Economic Research Papers 271314, University of Warwick - Department of Economics.

    Cited by:

    1. Bruno Ducoudré & Paul Hubert & Guilhem Tabarly, 2020. "The state-dependence of output revisions," Documents de Travail de l'OFCE 2020-04, Observatoire Francais des Conjonctures Economiques (OFCE).
    2. Francisco de Castro & Javier J. Pérez & Marta Rodríguez Vives, 2011. "Fiscal data revisions in Europe," Working Papers 1106, Banco de España.
    3. Ana Beatriz Galvão & James Mitchell, 2019. "Measuring Data Uncertainty: An Application using the Bank of England's "Fan Charts" for Historical GDP Growth," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2019-08, Economic Statistics Centre of Excellence (ESCoE).
    4. Wang, Yudong & Liu, Li & Wu, Chongfeng, 2017. "Forecasting the real prices of crude oil using forecast combinations over time-varying parameter models," Energy Economics, Elsevier, vol. 66(C), pages 337-348.
    5. Masolo, Riccardo M. & Paccagnini, Alessia, 2015. "Identifying noise shocks: a VAR with data revisions," LSE Research Online Documents on Economics 86314, London School of Economics and Political Science, LSE Library.
    6. 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.
    7. Asimakopoulos, Stylianos & Lalik, Magdalena & Paredes, Joan & Salvado García, José, 2023. "GDP revisions are not cool: the impact of statistical agencies’ trade-off," Working Paper Series 2857, European Central Bank.
    8. Michael P. Clements, 2014. "Anticipating Early Data Revisions to US GDP and the Effects of Releases on Equity Markets," ICMA Centre Discussion Papers in Finance icma-dp2014-06, Henley Business School, University of Reading.
    9. David Hendry & Michael P. Clements, 2010. "Forecasting from Mis-specified Models in the Presence of Unanticipated Location Shifts," Economics Series Working Papers 484, University of Oxford, Department of Economics.

  27. Clements, Michael P., 2008. "Explanations of the inconsistencies in survey respondents' forecasts," Economic Research Papers 269881, University of Warwick - Department of Economics.

    Cited by:

    1. Lui, Silvia & Mitchell, James & Weale, Martin, 2011. "The utility of expectational data: Firm-level evidence using matched qualitative-quantitative UK surveys," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1128-1146, October.
    2. Michael Clements, 2016. "Are Macroeconomic Density Forecasts Informative?," ICMA Centre Discussion Papers in Finance icma-dp2016-02, Henley Business School, University of Reading.
    3. Glas, Alexander, 2020. "Five dimensions of the uncertainty–disagreement linkage," International Journal of Forecasting, Elsevier, vol. 36(2), pages 607-627.
    4. Clements, Michael P., 2014. "Probability distributions or point predictions? Survey forecasts of US output growth and inflation," International Journal of Forecasting, Elsevier, vol. 30(1), pages 99-117.
    5. Manzan, Sebastiano, 2021. "Are professional forecasters Bayesian?," Journal of Economic Dynamics and Control, Elsevier, vol. 123(C).
    6. Franses, Ph.H.B.F. & Welz, M., 2018. "Evaluating heterogeneous forecasts for vintages of macroeconomic variables," Econometric Institute Research Papers EI2018-47, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    7. Pedersen, Michael, 2015. "What affects the predictions of private forecasters? The role of central bank forecasts in Chile," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1043-1055.
    8. Fernando Borraz & Laura Zacheo, 2018. "Inattention, Disagreement and Internal (In)Consistency of Inflation Forecasts," Documentos de trabajo 2018007, Banco Central del Uruguay.
    9. Geoff Kenny & Thomas Kostka & Federico Masera, 2015. "Density characteristics and density forecast performance: a panel analysis," Empirical Economics, Springer, vol. 48(3), pages 1203-1231, May.
    10. Clements, Michael P., 2011. "Do Professional Forecasters Pay Attention to Data Releases?," Economic Research Papers 270768, University of Warwick - Department of Economics.
    11. Boskabadi, Elahe, 2022. "Economic policy uncertainty and forecast bias in the survey of professional forecasters," MPRA Paper 115081, University Library of Munich, Germany.
    12. Nikola Mirkov & Andreas Steinhauer, 2014. "Are subjective distributions in inflation expectations symmetric?," ECON - Working Papers 173, Department of Economics - University of Zurich.
    13. Michael Clements, 2017. "Do forecasters target first or later releases of national accounts data?," ICMA Centre Discussion Papers in Finance icma-dp2017-03, Henley Business School, University of Reading.
    14. Clements, Michael P., 2008. "Rounding of probability forecasts: The SPF forecast probabilities of negative output growth," Economic Research Papers 269880, University of Warwick - Department of Economics.
    15. Michael P. Clements, 2022. "Forecaster Efficiency, Accuracy, and Disagreement: Evidence Using Individual‐Level Survey Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(2-3), pages 537-568, March.
    16. Zhao, Yongchen, 2023. "Internal consistency of household inflation expectations: Point forecasts vs. density forecasts," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1713-1735.
    17. Stanisławska, Ewa & Paloviita, Maritta & Łyziak, Tomasz, 2019. "Assessing reliability of aggregated inflation views in the European Commission consumer survey," Bank of Finland Research Discussion Papers 10/2019, Bank of Finland.
    18. Huang, Rong & Pilbeam, Keith & Pouliot, William, 2022. "Are macroeconomic forecasters optimists or pessimists? A reassessment of survey based forecasts," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 706-724.
    19. Kenneth C. Lichtendahl & Yael Grushka-Cockayne & Robert L. Winkler, 2013. "Is It Better to Average Probabilities or Quantiles?," Management Science, INFORMS, vol. 59(7), pages 1594-1611, July.
    20. William A. Branch, 2014. "Nowcasting and the Taylor Rule," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 46(5), pages 1035-1055, August.
    21. Zhao, Yongchen, 2024. "Uncertainty of household inflation expectations: Reconciling point and density forecasts," Economics Letters, Elsevier, vol. 234(C).
    22. Michael P. Clements, 2014. "US Inflation Expectations and Heterogeneous Loss Functions, 1968–2010," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(1), pages 1-14, January.
    23. Giulia Piccillo & Poramapa Poonpakdee, 2021. "Effects of Macro Uncertainty on Mean Expectation and Subjective Uncertainty: Evidence from Households and Professional Forecasters," CESifo Working Paper Series 9486, CESifo.
    24. 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.
    25. Sarah Miller & Patrick Sabourin, 2023. "What consistent responses on future inflation by consumers can reveal," Discussion Papers 2023-7, Bank of Canada.
    26. Wieland, Volker & Wolters, Maik H., 2010. "The diversity of forecasts from macroeconomic models of the U.S. economy," CFS Working Paper Series 2010/08, Center for Financial Studies (CFS).
    27. Clements, Michael P, 2012. "Subjective and Ex Post Forecast Uncertainty : US Inflation and Output Growth," The Warwick Economics Research Paper Series (TWERPS) 995, University of Warwick, Department of Economics.
    28. 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.
    29. Michael P. Clements, 2011. "An Empirical Investigation of the Effects of Rounding on the SPF Probabilities of Decline and Output Growth Histograms," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(1), pages 207-220, February.
    30. Victor Richmond R. Jose & Yael Grushka-Cockayne & Kenneth C. Lichtendahl, 2014. "Trimmed Opinion Pools and the Crowd's Calibration Problem," Management Science, INFORMS, vol. 60(2), pages 463-475, February.
    31. Maxime Phillot & Dr. Rina Rosenblatt-Wisch, 2018. "Inflation Expectations: The Effect of Question Ordering on Forecast Inconsistencies," Working Papers 2018-11, Swiss National Bank.
    32. Todd E. Clark & Gergely Ganics & Elmar Mertens, 2022. "What is the Predictive Value of SPF Point and Density Forecasts?," Working Papers 22-37, Federal Reserve Bank of Cleveland.
    33. Michael P. Clements, 2020. "Do Survey Joiners and Leavers Differ from Regular Participants? The US SPF GDP Growth and Inflation Forecasts," ICMA Centre Discussion Papers in Finance icma-dp2020-01, Henley Business School, University of Reading.
    34. Clements, Michael P. & Reade, J. James, 2020. "Forecasting and forecast narratives: The Bank of England Inflation Reports," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1488-1500.
    35. Conrad, Christian, 2017. "When does information on forecast variance improve the performance of a combined forecast?," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168200, Verein für Socialpolitik / German Economic Association.
    36. Michael P. Clements, 2020. "Are Some Forecasters’ Probability Assessments of Macro Variables Better Than Those of Others?," Econometrics, MDPI, vol. 8(2), pages 1-16, May.
    37. Deschamps, Bruno & Ioannidis, Christos, 2013. "Can rational stubbornness explain forecast biases?," Journal of Economic Behavior & Organization, Elsevier, vol. 92(C), pages 141-151.
    38. Nikola Mirkov & Andreas Steinhauer, 2015. "Ben Bernanke vs. Janet Yellen: Exploring the (a)symmetry of individual and aggregate inflation expectations," Working Papers 2015-10, Swiss National Bank.
    39. Fabian Kruger & Hendrik Plett, 2022. "Prediction intervals for economic fixed-event forecasts," Papers 2210.13562, arXiv.org, revised Mar 2024.
    40. Paloviita, Maritta & Virén, Matti, 2014. "Analysis of forecast errors in micro-level survey data," Bank of Finland Research Discussion Papers 8/2014, Bank of Finland.

  28. Michael P. Clements & Ana Beatriz Galvão, 2007. "Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US Output Growth," Working Papers 616, Queen Mary University of London, School of Economics and Finance.

    Cited by:

    1. 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.
    2. Pedregal, Diego J. & Pérez, Javier J., 2010. "Should quarterly government finance statistics be used for fiscal surveillance in Europe?," International Journal of Forecasting, Elsevier, vol. 26(4), pages 794-807, October.
    3. Thomas B. Götz & Alain Hecq & Jean‐Pierre Urbain, 2014. "Forecasting Mixed‐Frequency Time Series with ECM‐MIDAS Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(3), pages 198-213, April.
    4. 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.
    5. Pedregal, D.J. & Dejuán, O. & Gómez, N. & Tobarra, M.A., 2009. "Modelling demand for crude oil products in Spain," Energy Policy, Elsevier, vol. 37(11), pages 4417-4427, November.
    6. Diego J. Pedregal & Javier J. Pérez & A. Jesús Sánchez-Fuentes, 2014. "A toolkit to strengthen government budget surveillance," Working Papers 1416, Banco de España.
    7. 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.
    8. 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.
    9. Diego J. Pedregal & Javier J. Pérez & Antonio Sánchez Fuentes, 2014. "A Tookit to strengthen Government," Hacienda Pública Española / Review of Public Economics, IEF, vol. 211(4), pages 117-146, December.
    10. 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.

  29. Clements, Michael P., 2006. "Internal consistency of survey respondentsíforecasts: Evidence based on the Survey of Professional Forecasters," Economic Research Papers 269742, University of Warwick - Department of Economics.

    Cited by:

    1. Clements, Michael P., 2008. "Explanations of the inconsistencies in survey respondents'forecasts," The Warwick Economics Research Paper Series (TWERPS) 870, University of Warwick, Department of Economics.
    2. Clements, Michael P., 2008. "Rounding of probability forecasts: The SPF forecast probabilities of negative output growth," Economic Research Papers 269880, University of Warwick - Department of Economics.

  30. Clements, Michael P. & Galvao, Ana Beatriz & Kim, Jae H., 2006. "Quantile Forecasts of Daily Exchange Rate Returns from Forecasts of Realized Volatility," Economic Research Papers 269747, University of Warwick - Department of Economics.

    Cited by:

    1. Masato Ubukata & Toshiaki Watanabe, 2014. "Pricing Nikkei 225 Options Using Realized Volatility," The Japanese Economic Review, Japanese Economic Association, vol. 65(4), pages 431-467, December.
    2. Gerlach, Richard & Wang, Chao, 2020. "Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting, incorporating realized measures," International Journal of Forecasting, Elsevier, vol. 36(2), pages 489-506.
    3. Kawakami, Tabito, 2023. "Quantile prediction for Bitcoin returns using financial assets’ realized measures," Finance Research Letters, Elsevier, vol. 55(PA).
    4. Pedersen, Lasse Heje & Bollerslev, Tim & Hood, Benjamin & Huss, John, 2018. "Risk Everywhere: Modeling and Managing Volatility," CEPR Discussion Papers 12687, C.E.P.R. Discussion Papers.
    5. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2020. "The contribution of intraday jumps to forecasting the density of returns," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02505861, HAL.
    6. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "Are realized volatility models good candidates for alternative Value at Risk prediction strategies?," MPRA Paper 30364, University Library of Munich, Germany.
    7. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Post-Print halshs-01442618, HAL.
    8. Mehmet Pinar & Thanasis Stengos & M. Ege Yazgan, 2018. "Quantile forecast combination using stochastic dominance," Empirical Economics, Springer, vol. 55(4), pages 1717-1755, December.
    9. Frantiv{s}ek v{C}ech & Jozef Barun'ik, 2018. "Panel quantile regressions for estimating and predicting the Value--at--Risk of commodities," Papers 1807.11823, arXiv.org.
    10. Frantisek Cech & Jozef Barunik, 2017. "Measurement of Common Risk Factors: A Panel Quantile Regression Model for Returns," Papers 1708.08622, arXiv.org.
    11. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2020. "The contribution of intraday jumps to forecasting the density of returns," Post-Print halshs-02505861, HAL.
    12. Chortareas, Georgios & Jiang, Ying & Nankervis, John. C., 2011. "Forecasting exchange rate volatility using high-frequency data: Is the euro different?," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1089-1107, October.
    13. Makoto Takahashi & Toshiaki Watanabe & Yasuhiro Omori, 2014. "Volatility and Quantile Forecasts by Realized Stochastic Volatility Models with Generalized Hyperbolic Distribution," CIRJE F-Series CIRJE-F-949, CIRJE, Faculty of Economics, University of Tokyo.
    14. Guo, Yangli & He, Feng & Liang, Chao & Ma, Feng, 2022. "Oil price volatility predictability: New evidence from a scaled PCA approach," Energy Economics, Elsevier, vol. 105(C).
    15. Seo, Sung Won & Kim, Jun Sik, 2015. "The information content of option-implied information for volatility forecasting with investor sentiment," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 106-120.
    16. Degiannakis, Stavros, 2018. "Multiple Days Ahead Realized Volatility Forecasting: Single, Combined and Average Forecasts," MPRA Paper 96272, University Library of Munich, Germany.
    17. Filip Žikeš & Jozef Baruník, 2016. "Semi-parametric Conditional Quantile Models for Financial Returns and Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 14(1), pages 185-226.
    18. Corsi, Fulvio & Fusari, Nicola & La Vecchia, Davide, 2013. "Realizing smiles: Options pricing with realized volatility," Journal of Financial Economics, Elsevier, vol. 107(2), pages 284-304.
    19. Michael P. Clements & Ana Beatriz Galvao, 2009. "Forecasting US output growth using leading indicators: an appraisal using MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1187-1206.
    20. Bonato, Matteo & Caporin, Massimiliano & Ranaldo, Angelo, 2012. "Forecasting Realized (Co)Variances with a Bloc Structure Wishart Autoregressive Model," Working Papers on Finance 1211, University of St. Gallen, School of Finance.
    21. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, vol. 29(1), pages 28-42.
    22. Nektarios Aslanidis & Charlotte Christiansen, 2012. "Quantiles of the Realized Stock-Bond Correlation and Links to the Macroeconomy," CREATES Research Papers 2012-34, Department of Economics and Business Economics, Aarhus University.
    23. Vortelinos, Dimitrios I. & Lakshmi, Geeta, 2015. "Market risk of BRIC Eurobonds in the financial crisis period," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 295-310.
    24. Lazar, Emese & Xue, Xiaohan, 2020. "Forecasting risk measures using intraday data in a generalized autoregressive score framework," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1057-1072.
    25. Alexander, Carol & Kaeck, Andreas & Sumawong, Anannit, 2019. "A parsimonious parametric model for generating margin requirements for futures," European Journal of Operational Research, Elsevier, vol. 273(1), pages 31-43.
    26. Wen-Yuan Lin & I-Chun Tsai, 2016. "Asymmetric Fluctuating Behavior of China's Housing Prices," China & World Economy, Institute of World Economics and Politics, Chinese Academy of Social Sciences, vol. 24(2), pages 107-126, March.
    27. Hwang, Eunju & Shin, Dong Wan, 2013. "A CUSUM test for a long memory heterogeneous autoregressive model," Economics Letters, Elsevier, vol. 121(3), pages 379-383.
    28. Phan, Dinh Hoang Bach & Sharma, Susan Sunila & Narayan, Paresh Kumar, 2016. "Intraday volatility interaction between the crude oil and equity markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 40(C), pages 1-13.
    29. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2014. "Realized volatility models and alternative Value-at-Risk prediction strategies," Economic Modelling, Elsevier, vol. 40(C), pages 101-116.
    30. 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.
    31. Anjum, Hassan & Malik, Farooq, 2020. "Forecasting risk in the US Dollar exchange rate under volatility shifts," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    32. Fulvio Corsi & Davide Pirino & Roberto Renò, 2008. "Volatility forecasting: the jumps do matter," Department of Economics University of Siena 534, Department of Economics, University of Siena.
    33. Christian T. Brownlees & Giampiero Gallo, 2008. "Comparison of Volatility Measures: a Risk Management Perspective," Econometrics Working Papers Archive wp2008_03, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    34. Benoît Sévi, 2014. "Forecasting the volatility of crude oil futures using intraday data," Post-Print hal-01463921, HAL.
    35. Santos, Douglas G. & Candido, Osvaldo & Tófoli, Paula V., 2022. "Forecasting risk measures using intraday and overnight information," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    36. Hallam, Mark & Olmo, Jose, 2014. "Forecasting daily return densities from intraday data: A multifractal approach," International Journal of Forecasting, Elsevier, vol. 30(4), pages 863-881.
    37. Hwang, Eunju & Shin, Dong Wan, 2015. "A CUSUMSQ test for structural breaks in error variance for a long memory heterogeneous autoregressive model," Statistics & Probability Letters, Elsevier, vol. 99(C), pages 167-176.
    38. 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.
    39. Barbara Będowska-Sójka, 2018. "Is intraday data useful for forecasting VaR? The evidence from EUR/PLN exchange rate," Risk Management, Palgrave Macmillan, vol. 20(4), pages 326-346, November.
    40. Chao Wang & Richard Gerlach, 2019. "Semi-parametric Realized Nonlinear Conditional Autoregressive Expectile and Expected Shortfall," Papers 1906.09961, arXiv.org.
    41. Douglas G. Santos & Flavio A. Ziegelmann, 2014. "Volatility Forecasting via MIDAS, HAR and their Combination: An Empirical Comparative Study for IBOVESPA," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(4), pages 284-299, July.
    42. Ghysels, Eric & Ball, Ryan, 2017. "Automated Earnings Forecasts:- Beat Analysts or Combine and Conquer?," CEPR Discussion Papers 12179, C.E.P.R. Discussion Papers.
    43. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Documents de travail du Centre d'Economie de la Sorbonne 17006, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    44. Bee, Marco & Dupuis, Debbie J. & Trapin, Luca, 2016. "Realizing the extremes: Estimation of tail-risk measures from a high-frequency perspective," Journal of Empirical Finance, Elsevier, vol. 36(C), pages 86-99.
    45. Li, Z. & Hurn, A.S. & Clements, A.E., 2017. "Forecasting quantiles of day-ahead electricity load," Energy Economics, Elsevier, vol. 67(C), pages 60-71.
    46. Lin, Wen-Yuan & Tsai, I-Chun, 2019. "Black swan events in China's stock markets: Intraday price behaviors on days of volatility," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 395-411.
    47. F. Lilla, 2016. "High Frequency vs. Daily Resolution: the Economic Value of Forecasting Volatility Models," Working Papers wp1084, Dipartimento Scienze Economiche, Universita' di Bologna.
    48. Erik Kole & Thijs Markwat & Anne Opschoor & Dick van Dijk, 2017. "Forecasting Value-at-Risk under Temporal and Portfolio Aggregation," Journal of Financial Econometrics, Oxford University Press, vol. 15(4), pages 649-677.
    49. Andrada-Félix, Julián & Fernández-Rodríguez, Fernando & Fuertes, Ana-Maria, 2016. "Combining nearest neighbor predictions and model-based predictions of realized variance: Does it pay?," International Journal of Forecasting, Elsevier, vol. 32(3), pages 695-715.
    50. Jozef Barunik & Lubos Hanus, 2023. "Learning Probability Distributions of Day-Ahead Electricity Prices," Papers 2310.02867, arXiv.org, revised Oct 2023.
    51. Lee, Chien-Chiang & Lee, Cheng-Feng & Lee, Chi-Chuan, 2014. "Asymmetric dynamics in REIT prices: Further evidence based on quantile regression analysis," Economic Modelling, Elsevier, vol. 42(C), pages 29-37.
    52. Tae-Hwy Lee & Huiyu Huang, 2014. "Forecasting Value-at-Risk Using High Frequency Information," Working Papers 201409, University of California at Riverside, Department of Economics.
    53. Yang, Cai & Gong, Xu & Zhang, Hongwei, 2019. "Volatility forecasting of crude oil futures: The role of investor sentiment and leverage effect," Resources Policy, Elsevier, vol. 61(C), pages 548-563.
    54. Dimitrios P. Louzis & Spyros Xanthopoulos - Sissinis & Apostolos P. Refenes, 2012. "Stock index Value-at-Risk forecasting: A realized volatility extreme value theory approach," Economics Bulletin, AccessEcon, vol. 32(1), pages 981-991.
    55. Hwang, Eunju & Shin, Dong Wan, 2014. "Infinite-order, long-memory heterogeneous autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 339-358.
    56. Bjoern Schulte-Tillmann & Mawuli Segnon & Timo Wiedemann, 2023. "A comparison of high-frequency realized variance measures: Duration- vs. return-based approaches," CQE Working Papers 10523, Center for Quantitative Economics (CQE), University of Muenster.
    57. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    58. Jozef Barunik & Lubos Hanus, 2022. "Learning Probability Distributions in Macroeconomics and Finance," Papers 2204.06848, arXiv.org.
    59. Ana-Maria Fuertes & Jose Olmo, 2016. "On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?," JRFM, MDPI, vol. 9(3), pages 1-20, September.
    60. Dimitrios Louzis & Spyros Xanthopoulos-Sisinis & Apostolos Refenes, 2011. "Stock index realized volatility forecasting in the presence of heterogeneous leverage effects and long range dependence in the volatility of realized volatility," Post-Print hal-00709559, HAL.
    61. Ghysels, Eric & Ball, Ryan & Zhou, Huan, 2014. "Can we Automate Earnings Forecasts and Beat Analysts?," CEPR Discussion Papers 10186, C.E.P.R. Discussion Papers.
    62. Masato Ubukata & Toshiaki Watanabe, 2013. "Pricing Nikkei 225 Options Using Realized Volatility," Global COE Hi-Stat Discussion Paper Series gd12-273, Institute of Economic Research, Hitotsubashi University.
    63. Baruník, Jozef & Čech, František, 2021. "Measurement of common risks in tails: A panel quantile regression model for financial returns," Journal of Financial Markets, Elsevier, vol. 52(C).
    64. Ma, Feng & Wahab, M.I.M. & Zhang, Yaojie, 2019. "Forecasting the U.S. stock volatility: An aligned jump index from G7 stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 54(C), pages 132-146.
    65. Chorro, Christophe & Ielpo, Florian & Sévi, Benoît, 2020. "The contribution of intraday jumps to forecasting the density of returns," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    66. F. Lilla, 2017. "High Frequency vs. Daily Resolution: the Economic Value of Forecasting Volatility Models - 2nd ed," Working Papers wp1099, Dipartimento Scienze Economiche, Universita' di Bologna.
    67. Ubukata, Masato & Watanabe, Toshiaki, 2015. "Evaluating the performance of futures hedging using multivariate realized volatility," Journal of the Japanese and International Economies, Elsevier, vol. 38(C), pages 148-171.
    68. Zargar, Faisal Nazir & Kumar, Dilip, 2020. "Heterogeneous market hypothesis approach for modeling unbiased extreme value volatility estimator in presence of leverage effect: An individual stock level study with economic significance analysis," The Quarterly Review of Economics and Finance, Elsevier, vol. 77(C), pages 271-285.
    69. Ewald, Christian & Hadina, Jelena & Haugom, Erik & Lien, Gudbrand & Størdal, Ståle & Yahya, Muhammad, 2023. "Sample frequency robustness and accuracy in forecasting Value-at-Risk for Brent Crude Oil futures," Finance Research Letters, Elsevier, vol. 58(PA).
    70. 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.
    71. Hua, Jian & Manzan, Sebastiano, 2013. "Forecasting the return distribution using high-frequency volatility measures," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4381-4403.

  31. Clements, Michael P. & Galvao, Ana Beatriz, 2006. "Macroeconomic Forecasting with Mixed Frequency Data: Forecasting US output growth and inflation," Economic Research Papers 269743, University of Warwick - Department of Economics.

    Cited by:

    1. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    2. Afees A. Salisu & Ahamuefula Ephraim Ogbonna, 2017. "Improving the Predictive ability of oil for inflation: An ADL-MIDAS Approach," Working Papers 025, Centre for Econometric and Allied Research, University of Ibadan.
    3. C. Emre Alper & Salih Fendoglu & Burak Saltoglu, 2009. "MIDAS Volatility Forecast Performance Under Market Stress: Evidence from Emerging and Developed Stock Markets," Working Papers 2009/04, Bogazici University, Department of Economics.
    4. Anthony S. Tay, 2006. "Mixing Frequencies : Stock Returns as a Predictor of Real Output Growth," Macroeconomics Working Papers 22480, East Asian Bureau of Economic Research.
    5. Anthony S. Tay, 2007. "Financial Variables as Predictors of Real Output Growth," Development Economics Working Papers 22482, East Asian Bureau of Economic Research.
    6. Alper, C. Emre & Fendoglu, Salih & Saltoglu, Burak, 2008. "Forecasting Stock Market Volatilities Using MIDAS Regressions: An Application to the Emerging Markets," MPRA Paper 7460, University Library of Munich, Germany.
    7. Mihaela Bratu, 2012. "A Strategy to Improve the Survey of Professional Forecasters (SPF) Predictions Using Bias-Corrected-Accelerated (BCA) Bootstrap Forecast Intervals," International Journal of Synergy and Research, ToKnowPress, vol. 1(2), pages 45-59.
    8. Asgharian, Hossein & Hou, Ai Jun & Javed, Farrukh, 2013. "Importance of the macroeconomic variables for variance prediction A GARCH-MIDAS approach," Knut Wicksell Working Paper Series 2013/4, Lund University, Knut Wicksell Centre for Financial Studies.
    9. 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.
    10. 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.

  32. Clements, Michael P. & Harvey, David I., 2006. "Forecast Encompassing Tests and Probability Forecasts," Economic Research Papers 269744, University of Warwick - Department of Economics.

    Cited by:

    1. Clements, Michael P., 2008. "Explanations of the inconsistencies in survey respondents'forecasts," The Warwick Economics Research Paper Series (TWERPS) 870, University of Warwick, Department of Economics.
    2. 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.
    3. 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.
    4. Tsyplakov, Alexander, 2014. "Theoretical guidelines for a partially informed forecast examiner," MPRA Paper 55017, University Library of Munich, Germany.
    5. Turgut Kisinbay & Chikako Baba, 2011. "Predicting Recessions: A New Approach for Identifying Leading Indicators and Forecast Combinations," IMF Working Papers 2011/235, International Monetary Fund.
    6. Guizzardi, Andrea & Stacchini, Annalisa, 2015. "Real-time forecasting regional tourism with business sentiment surveys," Tourism Management, Elsevier, vol. 47(C), pages 213-223.
    7. Rodrigues, Bruno Dore & Stevenson, Maxwell J., 2013. "Takeover prediction using forecast combinations," International Journal of Forecasting, Elsevier, vol. 29(4), pages 628-641.
    8. Kajal Lahiri & Huaming Peng & Yongchen Zhao, 2013. "Testing the Value of Probability Forecasts for Calibrated Combining," Discussion Papers 13-02, University at Albany, SUNY, Department of Economics.
    9. Xiao, Liye & Wang, Jianzhou & Hou, Ru & Wu, Jie, 2015. "A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting," Energy, Elsevier, vol. 82(C), pages 524-549.
    10. Lahiri, Kajal & Wang, J. George, 2013. "Evaluating probability forecasts for GDP declines using alternative methodologies," International Journal of Forecasting, Elsevier, vol. 29(1), pages 175-190.
    11. Michael P. Clements, 2014. "Long-Run Restrictions and Survey Forecasts of Output, Consumption and Investment," ICMA Centre Discussion Papers in Finance icma-dp2014-02, Henley Business School, University of Reading.
    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. Stahl, Dale O., 2018. "Assessing the forecast performance of models of choice," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 73(C), pages 86-92.
    14. Timo Dimitriadis & Xiaochun Liu & Julie Schnaitmann, 2020. "Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary," Papers 2009.07341, arXiv.org.
    15. 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.
    16. Michael P. Clements, 2011. "An Empirical Investigation of the Effects of Rounding on the SPF Probabilities of Decline and Output Growth Histograms," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(1), pages 207-220, February.
    17. Michał Rubaszek & Paweł Skrzypczyński & Grzegorz Koloch, 2010. "Forecasting the Polish Zloty with Non-Linear Models," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 2(2), pages 151-167, March.
    18. Dimitriadis, Timo & Schnaitmann, Julie, 2021. "Forecast encompassing tests for the expected shortfall," International Journal of Forecasting, Elsevier, vol. 37(2), pages 604-621.
    19. Michael P. Clements, 2020. "Are Some Forecasters’ Probability Assessments of Macro Variables Better Than Those of Others?," Econometrics, MDPI, vol. 8(2), pages 1-16, May.
    20. Rendon-Sanchez, Juan F. & de Menezes, Lilian M., 2019. "Structural combination of seasonal exponential smoothing forecasts applied to load forecasting," European Journal of Operational Research, Elsevier, vol. 275(3), pages 916-924.
    21. Clements, Michael P., 2008. "Consensus and uncertainty: Using forecast probabilities of output declines," International Journal of Forecasting, Elsevier, vol. 24(1), pages 76-86.
    22. Timo Dimitriadis & Julie Schnaitmann, 2019. "Forecast Encompassing Tests for the Expected Shortfall," Papers 1908.04569, arXiv.org, revised Aug 2020.
    23. Lu Wang & Shan Li & Chao Liang, 2024. "Exploring the impact of oil security attention on oil volatility: A new perspective," International Finance, Wiley Blackwell, vol. 27(1), pages 61-80, April.

  33. 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.

    Cited by:

    1. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    2. Francisco Salas-Molina & Francisco J. Martin & Juan A. Rodr'iguez-Aguilar & Joan Serr`a & Josep Ll. Arcos, 2016. "Empowering cash managers to achieve cost savings by improving predictive accuracy," Papers 1605.04219, arXiv.org.
    3. Yavuz, Nilgün Çil & Yilanci, Veli, 2012. "Testing For Nonlinearity In G7 Macroeconomic Time Series," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 69-79, September.
    4. Stekler, H.O., 2007. "The future of macroeconomic forecasting: Understanding the forecasting process," International Journal of Forecasting, Elsevier, vol. 23(2), pages 237-248.
    5. Zapart, Christopher A., 2009. "On entropy, financial markets and minority games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(7), pages 1157-1172.
    6. He, Kaijian & Yu, Lean & Lai, Kin Keung, 2012. "Crude oil price analysis and forecasting using wavelet decomposed ensemble model," Energy, Elsevier, vol. 46(1), pages 564-574.
    7. Sznajderska, Anna, 2013. "On the empirical evidence of asymmetric effects in the Polish interest rate pass-through," The Journal of Economic Asymmetries, Elsevier, vol. 10(2), pages 78-93.
    8. Michal Franta, 2016. "The Effect of Nonlinearity between Credit Conditions and Economic Activity on Density Forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(2), pages 147-166, March.
    9. Nikolay Robinzonov & Klaus Wohlrabe, 2010. "Freedom of Choice in Macroeconomic Forecasting ," CESifo Economic Studies, CESifo Group, vol. 56(2), pages 192-220, June.
    10. Karstanje, Dennis & Sojli, Elvira & Tham, Wing Wah & van der Wel, Michel, 2013. "Economic valuation of liquidity timing," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5073-5087.
    11. Kasai, Ndahiriwe & Naraidoo, Ruthira, 2011. "Evaluating the forecasting performance of linear and nonlinear monetary policy rules for South Africa," MPRA Paper 40699, University Library of Munich, Germany.
    12. Francisco Salas-Molina & Juan A. Rodr'iguez-Aguilar & Joan Serr`a & Montserrat Guillen & Francisco J. Martin, 2016. "Empirical analysis of daily cash flow time series and its implications for forecasting," Papers 1611.04941, arXiv.org, revised Jun 2017.
    13. Rossen Anja, 2016. "On the Predictive Content of Nonlinear Transformations of Lagged Autoregression Residuals and Time Series Observations," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 236(3), pages 389-409, May.
    14. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.
    15. Martha Cecilia García & Aura María Jalal & Luis Alfonso Garzón & Jorge Mario López, 2013. "Métodos para predecir índices Bursátiles," Revista Ecos de Economía, Universidad EAFIT, December.
    16. Amendola, Alessandra & Christian, Francq, 2009. "Concepts and tools for nonlinear time series modelling," MPRA Paper 15140, University Library of Munich, Germany.
    17. Jennifer Castle & David Hendry, 2013. "Semi-automatic Non-linear Model selection," Economics Series Working Papers 654, University of Oxford, Department of Economics.
    18. 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.
    19. Dunz, Nepomuk & Naqvi, Asjad & Monasterolo, Irene, 2021. "Climate sentiments, transition risk, and financial stability in a stock-flow consistent model," Journal of Financial Stability, Elsevier, vol. 54(C).
    20. Bessec Marie & Bouabdallah Othman, 2005. "What Causes The Forecasting Failure of Markov-Switching Models? A Monte Carlo Study," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(2), pages 1-24, June.
    21. Emrah Önder & Ali Hepşen, 2013. "Combining Time Series Analysis and Multi Criteria Decision Making Techniques for Forecasting Financial Performance of Banks in Turkey," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 3(3), pages 530-530.
    22. Zuzanna Karolak, 2021. "Energy prices forecasting using nonlinear univariate models," Bank i Kredyt, Narodowy Bank Polski, vol. 52(6), pages 577-598.
    23. Henri Nyberg, 2018. "Forecasting US interest rates and business cycle with a nonlinear regime switching VAR model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(1), pages 1-15, January.
    24. Michael P. Clements & David F. Hendry, 2005. "Guest Editors’ Introduction: Information in Economic Forecasting," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 713-753, December.
    25. Massimo Guidolin, 2011. "Markov Switching Models in Empirical Finance," Advances in Econometrics, in: Missing Data Methods: Time-Series Methods and Applications, pages 1-86, Emerald Group Publishing Limited.
    26. Zhang, Pinyi & Ci, Bicong, 2020. "Deep belief network for gold price forecasting," Resources Policy, Elsevier, vol. 69(C).
    27. Kuosmanen, Petri & Nabulsi, Nasib & Vataja, Juuso, 2015. "Financial variables and economic activity in the Nordic countries," International Review of Economics & Finance, Elsevier, vol. 37(C), pages 368-379.
    28. Li, Dandan & Ghoshray, Atanu & Morley, Bruce, 2013. "An empirical study of nonlinear adjustment in the UIP model using a smooth transition regression model," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 109-120.
    29. He, Kaijian & Xu, Yang & Zou, Yingchao & Tang, Ling, 2015. "Electricity price forecasts using a Curvelet denoising based approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 425(C), pages 1-9.
    30. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.
    31. 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.
    32. Kaijian He & Rui Zha & Jun Wu & Kin Keung Lai, 2016. "Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price," Sustainability, MDPI, vol. 8(4), pages 1-11, April.
    33. Guidolin, Massimo & Hyde, Stuart & McMillan, David & Ono, Sadayuki, 2009. "Non-linear predictability in stock and bond returns: When and where is it exploitable?," International Journal of Forecasting, Elsevier, vol. 25(2), pages 373-399.
    34. Patrick T. kanda & Mehmet Balcilar & Pejman Bahramian & Rangan Gupta, 2014. "Forecasting South African Inflation Using Non-Linear Models: A Weighted Loss-Based Evaluation," Working Papers 201416, University of Pretoria, Department of Economics.
    35. Dunz, Nepomuk & Naqvi, Asjad & Monasterolo, Irene, 2019. "Climate Transition Risk, Climate Sentiments, and Financial Stability in a Stock-Flow Consistent approach," Ecological Economic Papers 23, WU Vienna University of Economics and Business.
    36. Luca Brugnolini & Antonello D’Agostino & Alex Tagliabracci, 2021. "Is Anything Predictable in Market-Based Surprises?," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 7(3), pages 387-410, November.
    37. Manzan, S. & Zerom, D., 2005. "A Multi-Step Forecast Density," CeNDEF Working Papers 05-05, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    38. María Clara Aristizábal Restrepo, 2006. "Evaluación asimétrica de una red neuronal artificial:Aplicación al caso de la inflación en Colombia," Borradores de Economia 377, Banco de la Republica de Colombia.
    39. He, Kaijian & Yu, Lean & Tang, Ling, 2015. "Electricity price forecasting with a BED (Bivariate EMD Denoising) methodology," Energy, Elsevier, vol. 91(C), pages 601-609.
    40. Kauppi, Heikki & Virtanen, Timo, 2021. "Boosting nonlinear predictability of macroeconomic time series," International Journal of Forecasting, Elsevier, vol. 37(1), pages 151-170.
    41. 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.
    42. Diteboho Xaba & Ntebogang Dinah Moroke & Ishmael Rapoo, 2019. "Modeling Stock Market Returns of BRICS with a Markov-Switching Dynamic Regression Model," Journal of Economics and Behavioral Studies, AMH International, vol. 11(3), pages 10-22.
    43. Fu, Zhonghao & Hong, Yongmiao, 2019. "A model-free consistent test for structural change in regression possibly with endogeneity," Journal of Econometrics, Elsevier, vol. 211(1), pages 206-242.
    44. Nava, Noemi & Di Matteo, Tiziana & Aste, Tomaso, 2018. "Financial time series forecasting using empirical mode decomposition and support vector regression," LSE Research Online Documents on Economics 91028, London School of Economics and Political Science, LSE Library.
    45. Teräsvirta, Timo, 2005. "Forecasting economic variables with nonlinear models," SSE/EFI Working Paper Series in Economics and Finance 598, Stockholm School of Economics, revised 29 Dec 2005.
    46. Costanza Torricelli & Marianna Brunetti, 2006. "Economic activity and Recession Probabilities: spread predictive power in Italy," Computing in Economics and Finance 2006 350, Society for Computational Economics.
    47. Noemi Nava & Tiziana Di Matteo & Tomaso Aste, 2018. "Financial Time Series Forecasting Using Empirical Mode Decomposition and Support Vector Regression," Risks, MDPI, vol. 6(1), pages 1-21, February.
    48. Arora Siddharth & Little Max A. & McSharry Patrick E., 2013. "Nonlinear and nonparametric modeling approaches for probabilistic forecasting of the US gross national product," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 395-420, September.
    49. He, Kaijian & Chen, Yanhui & Tso, Geoffrey K.F., 2018. "Forecasting exchange rate using Variational Mode Decomposition and entropy theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 15-25.
    50. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
    51. Dat Thanh Tran & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2021. "Bilinear Input Normalization for Neural Networks in Financial Forecasting," Papers 2109.00983, arXiv.org.
    52. Zaher Mundher Yaseen & Mazen Ismaeel Ghareb & Isa Ebtehaj & Hossein Bonakdari & Ridwan Siddique & Salim Heddam & Ali A. Yusif & Ravinesh Deo, 2018. "Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(1), pages 105-122, January.
    53. Yueling Xu & Wenyu Zhang & Haijun Bao & Shuai Zhang & Ying Xiang, 2019. "A SEM–Neural Network Approach to Predict Customers’ Intention to Purchase Battery Electric Vehicles in China’s Zhejiang Province," Sustainability, MDPI, vol. 11(11), pages 1-19, June.
    54. Cathy W. S. Chen & Richard H. Gerlach & Ann M. H. Lin, 2010. "Falling and explosive, dormant, and rising markets via multiple‐regime financial time series models," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 26(1), pages 28-49, January.
    55. Abakah, Emmanuel Joel Aikins & Gil-Alana, Luis Alberiko & Madigu, Godfrey & Romero-Rojo, Fatima, 2020. "Volatility persistence in cryptocurrency markets under structural breaks," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 680-691.
    56. Heikki Kauppi & Timo Virtanen, 2018. "Boosting Non-linear Predictabilityof Macroeconomic Time Series," Discussion Papers 124, Aboa Centre for Economics.
    57. Ahmad Alsharef & Sonia & Karan Kumar & Celestine Iwendi, 2022. "Time Series Data Modeling Using Advanced Machine Learning and AutoML," Sustainability, MDPI, vol. 14(22), pages 1-19, November.
    58. Jiang, He & Tao, Changqi & Dong, Yao & Xiong, Ren, 2021. "Robust low-rank multiple kernel learning with compound regularization," European Journal of Operational Research, Elsevier, vol. 295(2), pages 634-647.

  34. Hans-Martin Krolzig & Michael P. Clements & Department of Economics & University of Warwick, 2001. "Modelling Business Cycle Features Using Switching Regime Models," Economics Series Working Papers 58, University of Oxford, Department of Economics.

    Cited by:

    1. Melike Bildirici & Özgür Ömer Ersin, 2014. "Nonlinearity, Volatility and Fractional Integration in Daily Oil Prices: Smooth Transition Autoregressive ST-FI(AP)GARCH Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 108-135, October.
    2. 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.
    3. 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).

  35. David Hendry & Michael P. Clements, 2001. "Pooling of Forecasts," Economics Papers 2002-W9, Economics Group, Nuffield College, University of Oxford.

    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. Shaun P Vahey & Elizabeth C Wakerly, 2013. "Moving towards probability forecasting," BIS Papers chapters, in: Bank for International Settlements (ed.), Globalisation and inflation dynamics in Asia and the Pacific, volume 70, pages 3-8, Bank for International Settlements.
    3. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    4. Pablo Pincheira, 2008. "Combining Tests of Predictive Ability Theory and Evidence for Chilean and Canadian Exchange Rates," Working Papers Central Bank of Chile 459, Central Bank of Chile.
    5. Kajal Lahiri & Huaming Peng & Xuguang Simon Sheng, 2021. "Measuring Uncertainty of a Combined Forecast and Some Tests for Forecaster Heterogeneity," Working Papers 2021-005, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    6. Magnus, J.R. & Wang, W. & Zhang, Xinyu, 2012. "WALS Prediction," Discussion Paper 2012-043, Tilburg University, Center for Economic Research.
    7. Christopher Gibbs, 2015. "Forecast Combination, Non-linear Dynamics, and the Macroeconomy," Discussion Papers 2015-05, School of Economics, The University of New South Wales.
    8. 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.
    9. Panopoulou, Ekaterini & Vrontos, Spyridon, 2015. "Hedge fund return predictability; To combine forecasts or combine information?," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 103-122.
    10. Massimiliano Marcellino, 2005. "Pooling-based Data Interpolation and Backdating," Working Papers 299, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    11. 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.
    12. Marcellino, Massimliano, 2004. "Forecasting EMU macroeconomic variables," International Journal of Forecasting, Elsevier, vol. 20(2), pages 359-372.
    13. Ericsson, Neil R., 2017. "Economic forecasting in theory and practice: An interview with David F. Hendry," International Journal of Forecasting, Elsevier, vol. 33(2), pages 523-542.
    14. Dell'Aquila, Rosario & Ronchetti, Elvezio, 2006. "Stock and bond return predictability: the discrimination power of model selection criteria," Computational Statistics & Data Analysis, Elsevier, vol. 50(6), pages 1478-1495, March.
    15. Qin, Duo & He, Xinhua, 2012. "Modelling the impact of aggregate financial shocks external to the Chinese economy," BOFIT Discussion Papers 25/2012, Bank of Finland Institute for Emerging Economies (BOFIT).
    16. Chad Fulton & Kirstin Hubrich, 2021. "Forecasting US Inflation in Real Time," Econometrics, MDPI, vol. 9(4), pages 1-20, October.
    17. -, 2010. "Economics of climate change in Latin America and the Caribbean: summary 2010," Libros y Documentos Institucionales, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), number 2990 edited by Eclac.
    18. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    19. Wilson, Kevin J., 2017. "An investigation of dependence in expert judgement studies with multiple experts," International Journal of Forecasting, Elsevier, vol. 33(1), pages 325-336.
    20. 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.
    21. Ali B. Barlas & Seda Guler Mert & Berk Orkun Isa & Alvaro Ortiz & Tomasa Rodrigo & Baris Soybilgen & Ege Yazgan, 2024. "Big data financial transactions and GDP nowcasting: The case of Turkey," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 227-248, March.
    22. Mehmet Pinar & Thanasis Stengos & M. Ege Yazgan, 2018. "Quantile forecast combination using stochastic dominance," Empirical Economics, Springer, vol. 55(4), pages 1717-1755, December.
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    169. Zongwu Cai & Chaoqun Ma & Xianhua Mi, 2020. "Realized Volatility Forecasting Based on Dynamic Quantile Model Averaging," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202016, University of Kansas, Department of Economics, revised Sep 2020.
    170. Johannes Mayr & Dirk Ulbricht, 2007. "VAR Model Averaging for Multi-Step Forecasting," ifo Working Paper Series 48, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
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    1. KANGASSALO Pertti & TAKALA Kari, 2010. "Measuring the Usefulness of Consumers’ Inflation Expectations in Finland," EcoMod2003 330700076, EcoMod.
    2. Batchelor, Roy, 2007. "Bias in macroeconomic forecasts," International Journal of Forecasting, Elsevier, vol. 23(2), pages 189-203.
    3. Niu, Linlin & Xu, Xiu & Chen, Ying, 2015. "An adaptive approach to forecasting three key macroeconomic variables for transitional China," BOFIT Discussion Papers 12/2015, Bank of Finland Institute for Emerging Economies (BOFIT).
    4. Moosa, Imad A. & Vaz, John J., 2016. "Cointegration, error correction and exchange rate forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 44(C), pages 21-34.
    5. Keck, Alexander & Raubold, Alexander, 2006. "Forecasting trade," WTO Staff Working Papers ERSD-2006-05, World Trade Organization (WTO), Economic Research and Statistics Division.
    6. Shea, Paul, 2015. "Red herrings and revelations: does learning about a new variable worsen forecasts?," Economic Modelling, Elsevier, vol. 49(C), pages 395-406.
    7. Erik Olsen & Gavin Fay & Sarah Gaichas & Robert Gamble & Sean Lucey & Jason S Link, 2016. "Ecosystem Model Skill Assessment. Yes We Can!," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-24, January.
    8. Zeyyad Mandalinci, 2015. "Forecasting Inflation in Emerging Markets: An Evaluation of Alternative Models," CReMFi Discussion Papers 3, CReMFi, School of Economics and Finance, QMUL.
    9. Carlos Barros & Luis Gil-Alana, 2012. "Inflation forecasting in Angola: a fractional approach," CEsA Working Papers 103, CEsA - Centre for African and Development Studies.
    10. Zhang, YunQian, 2022. "Influence of stock market factors on the natural resources dependence for environmental change: Evidence from China," Resources Policy, Elsevier, vol. 77(C).
    11. Degiannakis, Stavros, 2017. "The one-trading-day-ahead forecast errors of intra-day realized volatility," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1298-1314.
    12. Barakchian , Seyed Mahdi, 2012. "Implications of Cointegration for Forecasting: A Review and an Empirical Analysis," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 7(1), pages 87-118, October.
    13. Oscar Claveria & Salvador Torra, 2013. "“Forecasting Business surveys indicators: neural networks vs. time series models”," IREA Working Papers 201320, University of Barcelona, Research Institute of Applied Economics, revised Nov 2013.
    14. Shahzad Ahmad & Farooq Pasha, 2015. "A Pragmatic Model for Monetary Policy Analysis I: The Case of Pakistan," SBP Research Bulletin, State Bank of Pakistan, Research Department, vol. 11, pages 1-42.
    15. 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.
    16. Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents’ expectations. Different patterns of anticipation of the 2008 financial crisis”," AQR Working Papers 201508, University of Barcelona, Regional Quantitative Analysis Group, revised Mar 2015.
    17. Roberta Capello & Andrea Caragliu, 2016. "After crisis scenarios for Europe: alternative evolutions of structural adjustments," Cambridge Journal of Regions, Economy and Society, Cambridge Political Economy Society, vol. 9(1), pages 81-101.
    18. Ahmet DEMIR & AtabekSHADMANOV & CumhurAYDINLI & Okan ERAY, 2015. "DESIGNING A FORECAST MODEL FOR ECONOMIC GROWTH OF JAPAN USING COMPETITIVE (HYBRID ANN VS MULTIPLE REGRESSION) MODELS Abstract : Artificial neural network models have been already used on many differen," EcoForum, "Stefan cel Mare" University of Suceava, Romania, Faculty of Economics and Public Administration - Economy, Business Administration and Tourism Department., vol. 4(2), pages 1-21, july.
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    21. 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.
    22. Huawei, Tian, 2022. "Does gross domestic product, inflation, total investment, and exchanges rate matter in natural resources commodity prices volatility," Resources Policy, Elsevier, vol. 79(C).
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    24. Kostas Bithas & Chrysostomos Stoforos, 2006. "Estimating Urban Residential Water Demand Determinants and Forecasting Water Demand for Athens Metropolitan Area, 2000-2010," South-Eastern Europe Journal of Economics, Association of Economic Universities of South and Eastern Europe and the Black Sea Region, vol. 4(1), pages 47-59.
    25. James M. Nason & Gregor W. Smith, 2005. "Identifying The New Keynesian Phillips Curve," Working Paper 1026, Economics Department, Queen's University.
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    28. Frommel, Michael & MacDonald, Ronald & Menkhoff, Lukas, 2005. "Markov switching regimes in a monetary exchange rate model," Economic Modelling, Elsevier, vol. 22(3), pages 485-502, May.
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    33. Diego Nocetti & William T. Smith, 2006. "Why Do Pooled Forecasts Do Better Than Individual Forecasts Ex Post?," Economics Bulletin, AccessEcon, vol. 4(36), pages 1-7.
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    35. Oliveira, Fernando Nascimento de, 2016. "Financial and Real Sector Leading Indicators of Recessions in Brazil Using Probabilistic Models," Revista Brasileira de Economia - RBE, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil), vol. 70(3), September.
    36. Pat McAllister & Graeme Newell & George Matysiak, 2005. "Analysing Uk Real Estate Market Forecast Disagreement," Real Estate & Planning Working Papers rep-wp2005-13, Henley Business School, University of Reading.
    37. Janine Aron & John Muellbauer, 2013. "New Methods for Forecasting Inflation, Applied to the US," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(5), pages 637-661, October.
    38. Jeff Schott & Jugal Kalita, 2011. "Neuro‐fuzzy time‐series analysis of large‐volume data," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(1), pages 39-57, January.
    39. Irfan Civcir, 2003. "The Monetary Models of the Turkish Lira/U.S. Dollar Exchange Rate: Long-run Relationships, Short-run Dynamics, and Forecasting," Eastern European Economics, Taylor & Francis Journals, vol. 41(6), pages 43-63, January.
    40. Roberta Capello & Andrea Caragliu & Ugo Fratesi, 2016. "The costs of the economic crisis: which scenario for the European regions?," Environment and Planning C, , vol. 34(1), pages 113-130, February.
    41. Petar Sorić & Ivana Lolić, 2015. "A note on forecasting euro area inflation: leave- $$h$$ h -out cross validation combination as an alternative to model selection," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 23(1), pages 205-214, March.
    42. David F. Hendry, 2004. "Unpredictability and the Foundations of Economic Forecasting," Econometric Society 2004 Australasian Meetings 27, Econometric Society.
    43. David Hendry & Michael P. Clements, 2001. "Pooling of Forecasts," Economics Papers 2002-W9, Economics Group, Nuffield College, University of Oxford.
    44. Friedrich Fritzer & Gabriel Moser & Johann Scharler, 2002. "Forecasting Austrian HICP and its Components using VAR and ARIMA Models," Working Papers 73, Oesterreichische Nationalbank (Austrian Central Bank).
    45. Fernando N. de Oliveira, 2015. "Financial and Real Sector Leading Indicators of Recessions in Brazil using Probabilistic Models," Working Papers Series 402, Central Bank of Brazil, Research Department.
    46. Jorge Sepúlveda-Velásquez & Pablo Tapia-Griñen & Boris Pastén-Henríquez, 2023. "Financial effects of natural disasters: a bibliometric analysis," 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. 118(3), pages 2691-2710, September.
    47. Papastamos, Dimitrios & Matysiak, George & Stevenson, Simon, 2015. "Assessing the accuracy and dispersion of real estate investment forecasts," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 141-152.
    48. Wu, Hong, 2022. "Trade openness, green finance and natural resources: A literature review," Resources Policy, Elsevier, vol. 78(C).
    49. Gantungalag Altansukh & Denise R. Osborn, 2022. "Using structural break inference for forecasting time series," Empirical Economics, Springer, vol. 63(1), pages 1-41, July.
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    51. Narayana, N.S.S. & Ghosh, Probal P., 2005. "Macroeconomic Simulation Results for India based on VEC/VAR Models," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 60(4), pages 1-40.
    52. Janine Aron & John Muellbauer, 2008. "New methods for forecasting inflation and its sub-components: application to the USA," Economics Series Working Papers 406, University of Oxford, Department of Economics.
    53. Dimitrios Papastamos & George Matysiak & Simon Stevenson, 2014. "A Comparative Analysis of the Accuracy and Uncertainty in Real Estate and Macroeconomic Forecasts," Real Estate & Planning Working Papers rep-wp2014-06, Henley Business School, University of Reading.
    54. Emmanuel Paroissien, 2020. "Forecasting bulk prices of Bordeaux wines using leading indicators," Post-Print hal-02408202, HAL.
    55. 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.
    56. Pat McAllister & Graeme Newell & George Matysiak, 2005. "An Evaluation Of The Performance Of UK Real Estate Forecasters," Real Estate & Planning Working Papers rep-wp2005-23, Henley Business School, University of Reading.
    57. Mellár, Tamás, 2016. "Szolgálólányból királycsináló - avagy az ökonometria makroökonómiai térhódítása? [From maidservant to king-maker - or the macroeconomic advance of econometrics?]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(3), pages 285-306.
    58. Teräsvirta, Timo, 2005. "Forecasting economic variables with nonlinear models," SSE/EFI Working Paper Series in Economics and Finance 598, Stockholm School of Economics, revised 29 Dec 2005.
    59. Duarte, Claudia & Rua, Antonio, 2007. "Forecasting inflation through a bottom-up approach: How bottom is bottom?," Economic Modelling, Elsevier, vol. 24(6), pages 941-953, November.
    60. Aron, Janine & Muellbauer, John, 2012. "Improving forecasting in an emerging economy, South Africa: Changing trends, long run restrictions and disaggregation," International Journal of Forecasting, Elsevier, vol. 28(2), pages 456-476.
    61. David F. Hendry, 2004. "Robustifying Forecasts from Equilibrium-Correction Models," Economics Papers 2004-W14, Economics Group, Nuffield College, University of Oxford.
    62. Manfred Deistler & Klaus Neusser, 2004. "Prognose uni- und multivariater Zeitreihen," Diskussionsschriften dp0401, Universitaet Bern, Departement Volkswirtschaft.
    63. Ming-Yuan Leon Li, 2008. "Hybrid versus highbred: combined economic models with time-series analyses," Quantitative Finance, Taylor & Francis Journals, vol. 8(6), pages 637-647.
    64. Mouchart, Michel & Rombouts, Jeroen V.K., 2005. "Clustered panel data models: an efficient approach for nowcasting from poor data," International Journal of Forecasting, Elsevier, vol. 21(3), pages 577-594.
    65. Paul Gallimore & Pat McAllister, 2005. "The Production and Consumption of Commercial Real Estate Market Forecasts," Real Estate & Planning Working Papers rep-wp2005-06, Henley Business School, University of Reading.
    66. Patrick McAllister & Paul Kennedy & Stephen Lee, 2007. "Data Dilemmas in Forecasting European Office Market Rents," Real Estate & Planning Working Papers rep-wp2007-10, Henley Business School, University of Reading.
    67. Tobias Knedlik & Rolf Scheufele, 2008. "Forecasting Currency Crises: Which Methods Signaled The South African Crisis Of June 2006?," South African Journal of Economics, Economic Society of South Africa, vol. 76(3), pages 367-383, September.
    68. Norman Swanson & Oleg Korenok, 2006. "The Incremental Predictive Information Associated with Using Theoretical New Keynesian DSGE Models Versus Simple Linear Alternatives," Departmental Working Papers 200615, Rutgers University, Department of Economics.
    69. Vincze, János & Bíró, Anikó & Elek, Péter, 2007. "Szimulációk és érzékenységvizsgálatok a magyar gazdaság egy középméretű makromodelljével [Simulations and sensitivity analyses with a medium-sized macro model of the Hungarian economy]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(9), pages 774-799.
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    72. Adam Elbourne & Henk Kranendonk & Rob Luginbuhl & Bert Smid & Martin Vromans, 2008. "Evaluating CPB's published GDP growth forecasts; a comparison with individual and pooled VAR based forecasts," CPB Document 172, CPB Netherlands Bureau for Economic Policy Analysis.
    73. Peter Öhman & Bo Söderberg & Ola Uhlin, 2011. "Accuracy of Swedish property appraisers’ forecasts of net operating income," Journal of Property Research, Taylor & Francis Journals, vol. 29(2), pages 103-122, November.
    74. Frank A.G. den Butter & Pieter W. Jansen, 2008. "Beating the Random Walk: a Performance Assessment of Long-term Interest Rate Forecasts," Tinbergen Institute Discussion Papers 08-102/3, Tinbergen Institute.
    75. Heilemann Ullrich, 2004. "Besser geht’s nicht – Genauigkeitsgrenzen von Konjunkturprognosen / As Good as it Gets – Limits of Accuracy of Macroeconomic Short Term Forecasts," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 224(1-2), pages 51-64, February.
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    1. Mehmet Pinar & Thanasis Stengos & M. Ege Yazgan, 2018. "Quantile forecast combination using stochastic dominance," Empirical Economics, Springer, vol. 55(4), pages 1717-1755, December.
    2. GIOT, Pierre & PETITJEAN, Mikael, 2005. "Dynamic asset allocation between stocks and bonds using the Bond-Equity Yield Ratio," LIDAM Discussion Papers CORE 2005010, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. GIOT, Pierre & PETITJEAN, Mikael, 2007. "The information content of the Bond-Equity Yield Ratio: Better than a random walk?," LIDAM Reprints CORE 1982, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Costas Milas & Ruthira Naraidoo, 2009. "Financial Market Conditions, Real Time, Nonlinearity and European Central Bank Monetary Policy: In-Sample and Out-of-Sample Assessment," Working Paper series 42_09, Rimini Centre for Economic Analysis.
    5. Angelos Kanas, 2003. "Non-linear forecasts of stock returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 299-315.
    6. Milas, Costas & Rothman, Philip, 2008. "Out-of-sample forecasting of unemployment rates with pooled STVECM forecasts," International Journal of Forecasting, Elsevier, vol. 24(1), pages 101-121.
    7. Hui Feng & Jia Liu, 2002. "A SETAR Model for Canadian GDP: Non-Linearities and Forecast Comparisons," Econometrics Working Papers 0206, Department of Economics, University of Victoria.
    8. Frédérique Bec & Annabelle de Gaye, 2019. "Le modèle autorégressif autorégressif à seuil avec effet rebond : Une application aux rendements boursiers français et américains ," Working Papers hal-02014663, HAL.
    9. Asmara Jamaleh, 2002. "Explaining and forecasting the euro/dollar exchange rate through a non-linear threshold model," The European Journal of Finance, Taylor & Francis Journals, vol. 8(4), pages 422-448.
    10. Manzan, Sebastiano & Zerom, Dawit, 2008. "A bootstrap-based non-parametric forecast density," International Journal of Forecasting, Elsevier, vol. 24(3), pages 535-550.
    11. 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.
    12. Bermejo Mancera, Miguel Ángel & Peña, Daniel & Sánchez, Ismael, 2009. "Graphical identification of TAR models," DES - Working Papers. Statistics and Econometrics. WS ws097723, Universidad Carlos III de Madrid. Departamento de Estadística.
    13. Amendola, Alessandra & Christian, Francq, 2009. "Concepts and tools for nonlinear time series modelling," MPRA Paper 15140, University Library of Munich, Germany.
    14. Li, Jing, 2011. "Bootstrap prediction intervals for SETAR models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 320-332, April.
    15. Baik, Hyeoncheol & Han, Sumin & Joo, Sunghoon & Lee, Kangbok, 2022. "A bank's optimal capital ratio: A time-varying parameter model to the partial adjustment framework," Journal of Banking & Finance, Elsevier, vol. 142(C).
    16. Frédérique Bec & Othman Bouabdallah & Laurent Ferrara, 2013. "The European Way out of Recession," Post-Print hal-02980626, HAL.
    17. 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.
    18. Zacharias Psaradakis & Fabio Spagnolo, 2005. "Forecast performance of nonlinear error-correction models with multiple regimes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(2), pages 119-138.
    19. Mehmet Pinar & Thanasis Stengos & M. Ege Yazgan, 2012. "Is there an optimal forecast combination? A stochastic dominance approach applied to the forecast combination puzzle," Working Papers 1206, University of Guelph, Department of Economics and Finance.
    20. Manzan, S. & Zerom, D., 2005. "A Multi-Step Forecast Density," CeNDEF Working Papers 05-05, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    21. Frédérique Bec & Othman Bouabdallah & Laurent Ferrara, 2014. "The way out of recessions: A forecasting analysis for some Euro area countries," Post-Print hal-02979744, HAL.
    22. Dimitrios I. Vortelinos & Konstantinos Gkillas, 2018. "Intraday realised volatility forecasting and announcements," International Journal of Banking, Accounting and Finance, Inderscience Enterprises Ltd, vol. 9(1), pages 88-118.
    23. Li, Jing, 2011. "Bootstrap prediction intervals for SETAR models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 320-332.
    24. Bob Nobay & Ivan Paya & David A. Peel, 2010. "Inflation Dynamics in the U.S.: Global but Not Local Mean Reversion," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(1), pages 135-150, February.
    25. Gianna Boero & Emanuela Marrocu, 2005. "Evaluating non-linear models on point and interval forecasts: an application with exchange rates," Banca Nazionale del Lavoro Quarterly Review, Banca Nazionale del Lavoro, vol. 58(232), pages 91-120.
    26. Ramazan Gencay & Ege Yazgan, 2017. "When Are Wavelets Useful Forecasters?," Working Papers 1704, The Center for Financial Studies (CEFIS), Istanbul Bilgi University.
    27. Rodriguez, Nestor & Eales, James S., 2015. "Structural Change via Threshold Effects: Estimating U.S. Meat Demand Using Smooth Transition Functions and the Effects of More Women in the Labor Force," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 206522, Agricultural and Applied Economics Association.
    28. Costas Milas & Phil Rothman, 2005. "Multivariate STAR Unemployment Rate Forecasts," Econometrics 0502010, University Library of Munich, Germany.
    29. Pedro M.D.C.B. Gouveia & Denise R. Osborn & Paulo M.M. Rodrigues, 2008. "Comparing Seasonal Forecasts of Industrial Production," Centre for Growth and Business Cycle Research Discussion Paper Series 102, Economics, The University of Manchester.
    30. Harun Özkan & M. Yazgan, 2015. "Is forecasting inflation easier under inflation targeting?," Empirical Economics, Springer, vol. 48(2), pages 609-626, March.
    31. Eleni Constantinou & Robert Georgiades & Avo Kazandjian & George Kouretas, 2005. "Regime Switching and Artificial Neural Network Forecasting," Working Papers 0502, University of Crete, Department of Economics.

  38. Clements, Michael P. & Smith, Jeremy, 1998. "Evaluating The Forecast Densities Of Linear And Non-Linear Models: Applications To Output Growth And Unemployment," Economic Research Papers 268791, University of Warwick - Department of Economics.

    Cited by:

    1. Norman R. Swanson & Nii Ayi Armah, 2011. "Predictive Inference Under Model Misspecification with an Application to Assessing the Marginal Predictive Content of Money for Output," Departmental Working Papers 201103, Rutgers University, Department of Economics.
    2. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    3. 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.
    4. G. Ascari & E. Marrocu, 2003. "Forecasting inflation: a comparison of linear Phillips curve models and nonlinear time serie models," Working Paper CRENoS 200307, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    5. Gabriela de Raaij & Burkhard Raunig, 2002. "Evaluating Density Forecasts with an Application to Stock Market Returns," Working Papers 59, Oesterreichische Nationalbank (Austrian Central Bank).
    6. Barbara Rossi & Tatevik Sekhposyan, 2013. "Evaluating predictive densities of U.S. output growth and inflation in a large macroeconomic data set," Economics Working Papers 1370, Department of Economics and Business, Universitat Pompeu Fabra.
    7. G. Boero & E. Marrocu, 2002. "The performance of Setar Models: a regime conditional evaluation of point, interval and density forecasts," Working Paper CRENoS 200208, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    8. Clements, Michael P & Krolzig, Hans-Martin, 2003. "Business Cycle Asymmetries: Characterization and Testing Based on Markov-Switching Autoregressions," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 196-211, January.
    9. Gabriela De Raaij & Burkhard Raunig, 2005. "Evaluating density forecasts from models of stock market returns," The European Journal of Finance, Taylor & Francis Journals, vol. 11(2), pages 151-166.
    10. Heather M. Anderson & Chin Nam Low, 2006. "Random Walk Smooth Transition Autoregressive Models," Contributions to Economic Analysis, in: Nonlinear Time Series Analysis of Business Cycles, pages 247-281, Emerald Group Publishing Limited.
    11. Gloria González-Rivera & Tae-Hwy Lee, 2007. "Nonlinear Time Series in Financial Forecasting," Working Papers 200803, University of California at Riverside, Department of Economics, revised Feb 2008.
    12. Francis X. Diebold & Jinyong Hahn & Anthony S. Tay, 1998. "Real-Time Multivariate Density Forecast Evaluation and Calibration: Monitoring the Risk of High-Frequency Returns on Foreign Exchange," Center for Financial Institutions Working Papers 99-05, Wharton School Center for Financial Institutions, University of Pennsylvania.
    13. Bhardwaj, Geetesh & Swanson, Norman R., 2006. "An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 539-578.
    14. Diep Duong & Norman Swanson, 2013. "Density and Conditional Distribution Based Specification Analysis," Departmental Working Papers 201312, Rutgers University, Department of Economics.
    15. Corradi, Valentina & Swanson, Norman R., 2006. "Bootstrap conditional distribution tests in the presence of dynamic misspecification," Journal of Econometrics, Elsevier, vol. 133(2), pages 779-806, August.
    16. Valentina Corradi & Norman Swanson, 2003. "The Block Bootstrap for Parameter Estimation Error In Recursive Estimation Schemes, With Applications to Predictive Evaluation," Departmental Working Papers 200313, Rutgers University, Department of Economics.
    17. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2008. "Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails," Discussion Papers 2008-10, School of Economics, The University of New South Wales.
    18. Shamiri, Ahmed & Shaari, Abu Hassan & Isa, Zaidi, 2007. "Practical Volatility Modeling for Financial Market Risk Management," MPRA Paper 9790, University Library of Munich, Germany, revised 15 May 2008.
    19. Manzan, S. & Zerom, D., 2005. "A Multi-Step Forecast Density," CeNDEF Working Papers 05-05, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    20. Amisano, Gianni & Giacomini, Raffaella, 2007. "Comparing Density Forecasts via Weighted Likelihood Ratio Tests," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 177-190, April.
    21. David Hendry, 2000. "A General Forecast-error Taxonomy," Econometric Society World Congress 2000 Contributed Papers 0608, Econometric Society.
    22. Corradi, Valentina & Swanson, Norman R., 2004. "Some recent developments in predictive accuracy testing with nested models and (generic) nonlinear alternatives," International Journal of Forecasting, Elsevier, vol. 20(2), pages 185-199.
    23. Warne, Anders & Coenen, Günter & Christoffel, Kai, 2010. "Forecasting with DSGE models," Working Paper Series 1185, European Central Bank.
    24. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
    25. Paulo M.M. Rodrigues & Nazarii Salish, 2011. "Modeling and Forecasting Interval Time Series with Threshold Models: An Application to S&P500 Index Returns," Working Papers w201128, Banco de Portugal, Economics and Research Department.
    26. Cai, Lili & Swanson, Norman R., 2011. "In- and out-of-sample specification analysis of spot rate models: Further evidence for the period 1982-2008," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 743-764, September.
    27. Shamiri, Ahmed & Shaari, Abu Hassan & Isa, Zaidi, 2008. "Comparing the accuracy of density forecasts from competing GARCH models," MPRA Paper 13662, University Library of Munich, Germany.
    28. Norman Swanson & Oleg Korenok, 2006. "The Incremental Predictive Information Associated with Using Theoretical New Keynesian DSGE Models Versus Simple Linear Alternatives," Departmental Working Papers 200615, Rutgers University, Department of Economics.
    29. Burkhard Raunig, 2003. "Testing for Longer Horizon Predictability of Return Volatility with an Application to the German," Working Papers 86, Oesterreichische Nationalbank (Austrian Central Bank).
    30. Laurent Ferrara & Dominique Guegan, 2006. "Real-time detection of the business cycle using SETAR models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00185372, HAL.
    31. Francisco Peñaranda, 2004. "Are Vector Autoregressions an Accurate Model for Dynamic Asset Allocation?," Working Papers wp2004_0419, CEMFI.

  39. Clements, Michael P. & Smith, Jeremy, 1998. "Non-Linearities In Exchange Rates," Economic Research Papers 268786, University of Warwick - Department of Economics.

    Cited by:

    1. G. Ascari & E. Marrocu, 2003. "Forecasting inflation: a comparison of linear Phillips curve models and nonlinear time serie models," Working Paper CRENoS 200307, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    2. G. Boero & E. Marrocu, 2000. "La performance di modelli non lineari per i tassi di cambio: un'applicazione con dati a diversa frequenza," Working Paper CRENoS 200014, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    3. Liew Khim Sen & Ahmad Zubaidi Baharumshah, 2003. "How Well the Ringgit-Yen Rate Fits the Non-linear Smooth Transition Autoregressive and Linear Autoregressive Models," GE, Growth, Math methods 0307004, University Library of Munich, Germany.
    4. G. Boero & E. Marrocu, 1999. "Modelli non lineari per i tassi di cambio: un confronto previsivo," Working Paper CRENoS 199914, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.

  40. Clements, Michael P. & Hendry, David F., 1998. "Forecasting With Difference-Stationary And Trend-Stationary Models," Economic Research Papers 268798, University of Warwick - Department of Economics.

    Cited by:

    1. Hamid Baghestani, 2009. "Evaluating random walk forecasts of exchange rates," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 26(3), pages 171-181, July.
    2. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    3. Shahzad Ahmad & Farooq Pasha, 2015. "A Pragmatic Model for Monetary Policy Analysis I: The Case of Pakistan," SBP Research Bulletin, State Bank of Pakistan, Research Department, vol. 11, pages 1-42.
    4. Pablo M. Pincheira & Carlos A. Medel, 2016. "Forecasting with a Random Walk," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 66(6), pages 539-564, December.
    5. Hansen, Bruce E., 2010. "Averaging estimators for autoregressions with a near unit root," Journal of Econometrics, Elsevier, vol. 158(1), pages 142-155, September.
    6. Pablo Pincheira & Andrés Gatty, 2014. "Forecasting Chilean Inflation with International Factors," Working Papers Central Bank of Chile 723, Central Bank of Chile.
    7. Peter C.B. Phillips, 2003. "Laws and Limits of Econometrics," Cowles Foundation Discussion Papers 1397, Cowles Foundation for Research in Economics, Yale University.
    8. Clements, Michael P., 2002. "Comments on 'The state of macroeconomic forecasting'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 469-482, December.
    9. David Griffiths, 2004. "The big problem of forecasting small change," Applied Economics, Taylor & Francis Journals, vol. 36(19), pages 2195-2207.
    10. Neil R. Ericsson, 2000. "Predictable uncertainty in economic forecasting," International Finance Discussion Papers 695, Board of Governors of the Federal Reserve System (U.S.).
    11. Guillaume Chevillon, 2004. ""Weak" trends for inference and forecasting in finite samples," Documents de Travail de l'OFCE 2004-12, Observatoire Francais des Conjonctures Economiques (OFCE).
    12. Luca Nocciola, "undated". "Finite sample forecast properties and window length under breaks in cointegrated systems," Discussion Papers 19/07, University of Nottingham, Granger Centre for Time Series Econometrics.
    13. Corradi, Valentina & Swanson, Norman R. & Olivetti, Claudia, 2001. "Predictive ability with cointegrated variables," Journal of Econometrics, Elsevier, vol. 104(2), pages 315-358, September.
    14. Alberto Baffigi & Roberto Golinelli & Giuseppe Parigi, 2002. "Real-time GDP forecasting in the euro area," Temi di discussione (Economic working papers) 456, Bank of Italy, Economic Research and International Relations Area.
    15. Neil R. Ericsson, 2001. "Forecast uncertainty in economic modeling," International Finance Discussion Papers 697, Board of Governors of the Federal Reserve System (U.S.).
    16. Carlos A. Medel & Michael Pedersen & Pablo M. Pincheira, 2016. "The Elusive Predictive Ability of Global Inflation," International Finance, Wiley Blackwell, vol. 19(2), pages 120-146, June.
    17. Muhammad Nadim Hanif & Muhammad Jahanzeb Malik, 2015. "Evaluating the Performance of Inflation Forecasting Models of Pakistan," SBP Research Bulletin, State Bank of Pakistan, Research Department, vol. 11, pages 43-78.
    18. Yin-Wong Cheung & Menzie D. Chinn & Antonio Garcia-Pascual, 2005. "Empirical Exchange Rate Models of the Nineties: Are Any Fit to Survive?," Working Papers 122005, Hong Kong Institute for Monetary Research.
    19. Corradi, V. & Swanson, N.R., 2000. "A Consistent Test for Nonlinear Out of Sample Predictive Accuracy," Discussion Papers 0012, University of Exeter, Department of Economics.
    20. J. Doyne Farmer & Francois Lafond, 2015. "How predictable is technological progress?," Papers 1502.05274, arXiv.org, revised Nov 2015.
    21. Chunming Yuan, 2008. "The Exchange Rate and Macroeconomic Determinants: Time-Varying Transitional Dynamics," UMBC Economics Department Working Papers 09-114, UMBC Department of Economics, revised 01 Nov 2009.
    22. Yan-Leung Cheung & Yin-Wong Cheung & Alan T. K. Wan, 2009. "A High-Low Model of Daily Stock Price Ranges," Working Papers 032009, Hong Kong Institute for Monetary Research.
    23. 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.
    24. David Harvey & Terence Mills, 2002. "Unit roots and double smooth transitions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(5), pages 675-683.
    25. Lafond, François & Bailey, Aimee Gotway & Bakker, Jan David & Rebois, Dylan & Zadourian, Rubina & McSharry, Patrick & Farmer, J. Doyne, 2018. "How well do experience curves predict technological progress? A method for making distributional forecasts," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 104-117.
    26. Pincheira, Pablo & Selaive, Jorge & Nolazco, Jose Luis, 2016. "The Evasive Predictive Ability of Core Inflation," MPRA Paper 68704, University Library of Munich, Germany.
    27. Baffigi, Alberto & Golinelli, Roberto & Parigi, Giuseppe, 2004. "Bridge models to forecast the euro area GDP," International Journal of Forecasting, Elsevier, vol. 20(3), pages 447-460.
    28. Le Pen, Yannick & Sévi, Benoît, 2010. "What trends in energy efficiencies? Evidence from a robust test," Energy Economics, Elsevier, vol. 32(3), pages 702-708, May.
    29. Varang Wiriyawit, 2014. "Trend Mis-specifications and Estimated Policy Implications in DSGE Models," ANU Working Papers in Economics and Econometrics 2014-615, Australian National University, College of Business and Economics, School of Economics.
    30. Clements, Michael P. & Kim, Jae H., 2007. "Bootstrap prediction intervals for autoregressive time series," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3580-3594, April.
    31. K. G. Sorokozherdyev & K. A. Khodosov, 2020. "The Influence of the Regional Sectoral Structure on the Socio-Economic Development of Primorye Region," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 19(1), pages 60-78.

  41. Clements, Michael & Krolzig, Hans-Martin, 1998. "Business Cycle Asymmetries: Characterisation and Testing based on Markov-Switching Autoregressions," Economic Research Papers 269248, University of Warwick - Department of Economics.

    Cited by:

    1. Dufrénot, Gilles & Malik, Sheheryar, 2012. "The changing role of house price dynamics over the business cycle," Economic Modelling, Elsevier, vol. 29(5), pages 1960-1967.
    2. Camacho, Maximo, 2011. "Markov-switching models and the unit root hypothesis in real US GDP," Economics Letters, Elsevier, vol. 112(2), pages 161-164, August.
    3. Bec, Frédérique & Bouabdallah, Othman & Ferrara, Laurent, 2015. "Comparing the shape of recoveries: France, the UK and the US," Economic Modelling, Elsevier, vol. 44(C), pages 327-334.
    4. Knüppel, Malte, 2004. "Testing for business cycle asymmetries based on autoregressions with a Markov-switching intercept," Discussion Paper Series 1: Economic Studies 2004,41, Deutsche Bundesbank.
    5. Jorge Andrés Tamayo Castaño, 2012. "Asimetrías en la demanda por trabajo en Colombia: el papel del ciclo económico," Borradores de Economia 9286, Banco de la Republica.
    6. Lee, Hwa-Taek & Yoon, Gawon, 2007. "Does Purchasing Power Parity Hold Sometimes? Regime Switching in Real Exchange Rates," Economics Working Papers 2007-24, Christian-Albrechts-University of Kiel, Department of Economics.
    7. Milan Christian de Wet, 2021. "Modelling the Australasian Financial Cycle: A Markov-Regime Switching Approach," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 14(1), pages 69-79, June.
    8. Cem Cakmakli & Richard Paap & Dick J.C. van Dijk, 2011. "Modeling and Estimation of Synchronization in Multistate Markov-Switching Models," Tinbergen Institute Discussion Papers 11-002/4, Tinbergen Institute.
    9. 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.
    10. Paap, R. & Segers, R. & van Dijk, D.J.C., 2007. "Do leading indicators lead peaks more than troughs?," Econometric Institute Research Papers EI 2007-08, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    11. Laurent Ferrara & Dominique Guégan, 2006. "Detection of the Industrial Business Cycle using SETAR Models," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(3), pages 353-371.
    12. Darné, O. & Ferrara, L., 2009. "Identification of slowdowns and accelerations for the euro area economy," Working papers 239, Banque de France.
    13. Adewale Hassan & Daniel Meyer, 2021. "Exploring the Channels of Transmission between External Debt and Economic Growth: Evidence from Sub-Saharan African Countries," Economies, MDPI, vol. 9(2), pages 1-16, April.
    14. Amélie Charles & Olivier Darné & Claude Diebolt & Laurent Ferrara, 2012. "A new monthly chronology of the US industrial cycles in the prewar economy," Working Papers 12-02, Association Française de Cliométrie (AFC).
    15. Rothman Philip A, 2008. "Reconsideration of the Markov Chain Evidence on Unemployment Rate Asymmetry," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(3), pages 1-18, September.
    16. Milas, Costas & Rothman, Philip, 2008. "Out-of-sample forecasting of unemployment rates with pooled STVECM forecasts," International Journal of Forecasting, Elsevier, vol. 24(1), pages 101-121.
    17. Claude Diebolt & Jamel Trabelsi, 2008. "Human Capital and French Macroeconomic Growth in the Long Run," Working Papers 08-11, Association Française de Cliométrie (AFC).
    18. Gambetti, Luca & Messina, Julián, 2017. "Evolving Wage Cyclicality in Latin America," IZA Discussion Papers 10657, Institute of Labor Economics (IZA).
    19. Cem Cakmakli & Richard Paap & Dick van Dijk, 2011. "Measuring and Predicting Heterogeneous Recessions," Tinbergen Institute Discussion Papers 11-154/4, Tinbergen Institute, revised 15 Nov 2011.
    20. 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".
    21. Fernando Delbianco & Andrés Fioriti & Fernando Tohmé, 2023. "Markov chains, eigenvalues and the stability of economic growth processes," Empirical Economics, Springer, vol. 64(3), pages 1347-1373, March.
    22. Marie Adanero-Donderis & Olivier Darné & Laurent Ferrara, 2009. "Un indicateur probabiliste du cycle d’accélération pour l’économie française," Économie et Prévision, Programme National Persée, vol. 189(3), pages 95-114.
    23. Peter McAdam, 2007. "USA, Japan and the Euro Area: Comparing Business-Cycle Features," International Review of Applied Economics, Taylor & Francis Journals, vol. 21(1), pages 135-156.
    24. Chen, Shiu-Sheng, 2011. "Lack of consumer confidence and stock returns," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 225-236, March.
    25. 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.
    26. Laurent Ferrara & Dominique Guegan, 2006. "Real-time detection of the business cycle using SETAR models," Post-Print halshs-00185372, HAL.
    27. Alogoskoufis, George & Malliaris, A.G. & Stengos, Thanasis, 2023. "The scope and methodology of economic and financial asymmetries," The Journal of Economic Asymmetries, Elsevier, vol. 27(C).
    28. Coakley, Jerry & Fuertes, Ana-Maria, 2006. "Testing for sign and amplitude asymmetries using threshold autoregressions," Journal of Economic Dynamics and Control, Elsevier, vol. 30(4), pages 623-654, April.
    29. Chen, Shyh-Wei, 2007. "Measuring business cycle turning points in Japan with the Markov Switching Panel model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 76(4), pages 263-270.
    30. Bessec Marie & Bouabdallah Othman, 2005. "What Causes The Forecasting Failure of Markov-Switching Models? A Monte Carlo Study," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(2), pages 1-24, June.
    31. Hwa-Taek Lee & Gawon Yoon, 2013. "Does purchasing power parity hold sometimes? Regime switching in real exchange rates," Applied Economics, Taylor & Francis Journals, vol. 45(16), pages 2279-2294, June.
    32. Harding, Don, 2008. "Detecting and forecasting business cycle turning points," MPRA Paper 33583, University Library of Munich, Germany.
    33. Knüppel, Malte, 2008. "Can capacity constraints explain asymmetries," Discussion Paper Series 1: Economic Studies 2008,01, Deutsche Bundesbank.
    34. Harding, Don & Pagan, Adrian, 2001. "Extracting, Using and Analysing Cyclical Information," MPRA Paper 15, University Library of Munich, Germany.
    35. Charlotte Le Chapelain, 2012. "Allocation des talents et accumulation de capital humain en France à la fin du XIXe siècle," Working Papers 12-03, Association Française de Cliométrie (AFC).
    36. Sandberg, Rickard, 2016. "Trends, unit roots, structural changes, and time-varying asymmetries in U.S. macroeconomic data: the Stock and Watson data re-examined," Economic Modelling, Elsevier, vol. 52(PB), pages 699-713.
    37. Kamel Helali, 2022. "Markov Switching-Vector AutoRegression Model Analysis of the Economic and Growth Cycles in Tunisia and Its Main European Partners," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 13(1), pages 656-686, March.
    38. Chen, Bin & Hong, Yongmiao, 2014. "A unified approach to validating univariate and multivariate conditional distribution models in time series," Journal of Econometrics, Elsevier, vol. 178(P1), pages 22-44.
    39. McCAUSLAND, William J., 2004. "Time Reversibility of Stationary Regular Finite State Markov Chains," Cahiers de recherche 09-2004, Centre interuniversitaire de recherche en économie quantitative, CIREQ.
    40. M. Portugal & I.A. de Morais, 2004. "STRUCTURAL CHANGE IN THE BRAZILIAN DEMAND FOR IMPORTS: A regime switching approach," Econometric Society 2004 Latin American Meetings 346, Econometric Society.
    41. Mile Bosnjak, 2017. "Structural Change In Croatian Real Gdp Growth Rates," Economic Thought and Practice, Department of Economics and Business, University of Dubrovnik, vol. 26(1), pages 205-218, june.
    42. Clements Michael P., 2012. "Forecasting U.S. Output Growth with Non-Linear Models in the Presence of Data Uncertainty," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(1), pages 1-27, January.
    43. Cruz-Rodríguez, Alexis, 2004. "Un análisis del ciclo económico de la República Dominicana bajo cambios de régimen [Analysis of business cycle of the Dominican Republic using Markov Switching model]," MPRA Paper 54352, University Library of Munich, Germany.
    44. Moradi, Alireza, 2016. "Modeling Business Cycle Fluctuations through Markov Switching VAR:An Application to Iran," MPRA Paper 73608, University Library of Munich, Germany.
    45. Emilio Zanetti Chini, 2018. "Forecasting dynamically asymmetric fluctuations of the U.S. business cycle," CREATES Research Papers 2018-13, Department of Economics and Business Economics, Aarhus University.
    46. Almeida, Pedro Cameira de & Fuinhas, José Alberto & Marques, António Cardoso, 2011. "A assimetria dos ciclos económicos: Evidência internacional usando o teste triples [The asymmetry of business cycles: International evidence using triples test]," MPRA Paper 35208, University Library of Munich, Germany.
    47. Cruz-Rodríguez, Alexis, 2005. "Ciclos Económicos Sincronizados y Uniones Monetarias en Centroamérica y la República Dominicana [Business Cycles Synchronisation and Monetary Union in Central American and the Dominican Republic]," MPRA Paper 72104, University Library of Munich, Germany.
    48. Artur Silva Lopes & Gabriel Florin Zsurkis, 2019. "Are linear models really unuseful to describe business cycle data?," Applied Economics, Taylor & Francis Journals, vol. 51(22), pages 2355-2376, May.
    49. Belaire-Franch Jorge & Contreras Dulce, 2003. "An Assessment of International Business Cycle Asymmetries using Clements and Krolzig's Parametric Approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 6(4), pages 1-11, March.
    50. Marcelo Savino Portugal & Igor Alexandre Clemente de Morais, 2004. "Business Cycle In The Industrial Production Of Brazilian States," Econometric Society 2004 Latin American Meetings 23, Econometric Society.
    51. Firouz Fallahi & Gabriel Rodríguez, 2007. "Using Markov-Switching Models to Identify the Link between Unemployment and Criminality," Working Papers 0701E, University of Ottawa, Department of Economics.
    52. Cifter, Atilla & Yilmazer, Sait & Cifter, Elif, 2009. "Analysis of sectoral credit default cycle dependency with wavelet networks: Evidence from Turkey," Economic Modelling, Elsevier, vol. 26(6), pages 1382-1388, November.
    53. McKay, Alisdair, 2006. "The Brevity and Violence of Contractions and Expansions," CEPR Discussion Papers 5756, C.E.P.R. Discussion Papers.
    54. Milan Christian Wet & Ilse Botha, 2022. "Constructing and Characterising the Aggregate South African Financial Cycle: A Markov Regime-Switching Approach," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(1), pages 37-67, March.
    55. 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.
    56. Ferrara, Laurent, 2006. "A real-time recession indicator for the Euro area," MPRA Paper 4042, University Library of Munich, Germany.
    57. Frédérick Demers & Ryan Macdonald, 2007. "The Canadian Business Cycle: A Comparison of Models," Staff Working Papers 07-38, Bank of Canada.
    58. Iiboshi, Hirokuni, 2007. "Duration dependence of the business cycle in Japan: A Bayesian analysis of extended Markov switching model," Japan and the World Economy, Elsevier, vol. 19(1), pages 86-111, January.
    59. Medhioub, Imed, 2007. "Asymétrie des cycles économiques et changement de régimes : cas de la Tunisie," L'Actualité Economique, Société Canadienne de Science Economique, vol. 83(4), pages 529-553, décembre.
    60. Moritz Cruz, 2005. "The business cycle in a financially deregulated context: Theory and evidence," International Review of Applied Economics, Taylor & Francis Journals, vol. 19(3), pages 271-287.
    61. Siem Jan Koopman & Kai Ming Lee, 2005. "Measuring Asymmetric Stochastic Cycle Components in U.S. Macroeconomic Time Series," Tinbergen Institute Discussion Papers 05-081/4, Tinbergen Institute.
    62. Sylvia Kaufmann, 2016. "Hidden Markov models in time series, with applications in economics," Working Papers 16.06, Swiss National Bank, Study Center Gerzensee.
    63. James Morley & Jeremy M. Piger, 2005. "The importance of nonlinearity in reproducing business cycle features," Working Papers 2004-032, Federal Reserve Bank of St. Louis.
    64. Silva Lopes, Artur C. & Florin Zsurkis, Gabriel, 2015. "Revisiting non-linearities in business cycles around the world," MPRA Paper 65668, University Library of Munich, Germany.
    65. 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.
    66. 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.
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    Cited by:

    1. Moritz Cruz, 2005. "A three-regime business cycle model for an emerging economy," Applied Economics Letters, Taylor & Francis Journals, vol. 12(7), pages 399-402.
    2. Philip Rothman, "undated". "Higher-Order Residual Analysis for Simple Bilinear and Threshold Autoregressive Models with the TR Test," Working Papers 9813, East Carolina University, Department of Economics.
    3. Nissilä, Wilma, 2020. "Probit based time series models in recession forecasting – A survey with an empirical illustration for Finland," BoF Economics Review 7/2020, Bank of Finland.
    4. Elano Ferreira Arruda & Roberto Tatiwa Ferreira & Ivan Castelar, 2008. "Modelos lineares e não lineares da curva de Phillips para previsão da taxa de Inflação no Brasil," Anais do XXXVI Encontro Nacional de Economia [Proceedings of the 36th Brazilian Economics Meeting] 200807211607140, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    5. Moritz Cruz, 2005. "The business cycle in a financially deregulated context: Theory and evidence," International Review of Applied Economics, Taylor & Francis Journals, vol. 19(3), pages 271-287.

  43. Clements, Michael P. & Krolzig, Hans-Martin, 1997. "A Comparison Of The Forecast Performance Of Markov-Switching And Threshold Autoregressive Models Of Us Gnp," Economic Research Papers 268771, University of Warwick - Department of Economics.

    Cited by:

    1. Driffill, John & Sola, Martin & Kenc, Turalay & Spagnolo, Fabio, 2004. "On Model Selection and Markov Switching: A Empirical Examination of Term Structure Models with Regime Shifts," CEPR Discussion Papers 4165, C.E.P.R. Discussion Papers.
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    3. Dick van Dijk & Timo Terasvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models — A Survey Of Recent Developments," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 1-47.
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    5. Andrea Bucci, 2020. "Realized Volatility Forecasting with Neural Networks," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 502-531.
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    8. Lee, Hwa-Taek & Yoon, Gawon, 2007. "Does Purchasing Power Parity Hold Sometimes? Regime Switching in Real Exchange Rates," Economics Working Papers 2007-24, Christian-Albrechts-University of Kiel, Department of Economics.
    9. 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.
    10. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
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    Cited by:

    1. Dick van Dijk & Timo Terasvirta & Philip Hans Franses, 2002. "Smooth Transition Autoregressive Models — A Survey Of Recent Developments," Econometric Reviews, Taylor & Francis Journals, vol. 21(1), pages 1-47.
    2. Terui, Nobuhiko & van Dijk, Herman K., 2002. "Combined forecasts from linear and nonlinear time series models," International Journal of Forecasting, Elsevier, vol. 18(3), pages 421-438.
    3. Jha, Girish K. & Sinha, Kanchan, 2013. "Agricultural Price Forecasting Using Neural Network Model: An Innovative Information Delivery System," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 26(2).
    4. McMillan, David G., 2009. "The confusing time-series behaviour of real exchange rates: Are asymmetries important?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 19(4), pages 692-711, October.
    5. Pentti Saikkonen & Antti Ripatti, 2000. "On the Estimation of Euler Equations in the Presence of a Potential Regime Shift," Manchester School, University of Manchester, vol. 68(s1), pages 92-121.
    6. David McMillan, 2008. "Non-linear cointegration and adjustment: an asymmetric exponential smooth-transition model for US interest rates," Empirical Economics, Springer, vol. 35(3), pages 591-606, November.
    7. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    8. Bauwens, Luc & Braione, Manuela & Storti, Giuseppe, 2017. "A dynamic component model for forecasting high-dimensional realized covariance matrices," Econometrics and Statistics, Elsevier, vol. 1(C), pages 40-61.
    9. Fabio Gobbi, 2021. "Evaluating Forecasts from State-Dependent Autoregressive Models for US GDP Growth Rate. Comparison with Alternative Approaches," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 11(6), pages 1-7.
    10. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521770415, November.
    11. 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.
    12. Hui Feng & Jia Liu, 2002. "A SETAR Model for Canadian GDP: Non-Linearities and Forecast Comparisons," Econometrics Working Papers 0206, Department of Economics, University of Victoria.
    13. Ahmad, Saad & Civelli, Andrea, 2016. "Globalization and inflation: A threshold investigation," Journal of Macroeconomics, Elsevier, vol. 48(C), pages 283-304.
    14. Lanne, Markku, 1999. "Testing the expectations hypothesis of the term structure of interest rates in the presence of a potential regime shift," Bank of Finland Research Discussion Papers 20/1999, Bank of Finland.
    15. G. Boero & E. Marrocu, 2002. "The performance of Setar Models: a regime conditional evaluation of point, interval and density forecasts," Working Paper CRENoS 200208, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    16. Manzan, Sebastiano & Zerom, Dawit, 2008. "A bootstrap-based non-parametric forecast density," International Journal of Forecasting, Elsevier, vol. 24(3), pages 535-550.
    17. Michael Dueker & Martin Sola & Fabio Spagnolo, 2006. "Contemporaneous Threshold Autoregressive Models: Estimation, Testing and Forecasting," Department of Economics Working Papers 2006-04, Universidad Torcuato Di Tella.
    18. Rapach, David E. & Wohar, Mark E., 2006. "The out-of-sample forecasting performance of nonlinear models of real exchange rate behavior," International Journal of Forecasting, Elsevier, vol. 22(2), pages 341-361.
    19. Amendola, Alessandra & Christian, Francq, 2009. "Concepts and tools for nonlinear time series modelling," MPRA Paper 15140, University Library of Munich, Germany.
    20. 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.
    21. Philippe Goulet Coulombe, 2020. "The Macroeconomy as a Random Forest," Papers 2006.12724, arXiv.org, revised Mar 2021.
    22. Li, Jing, 2011. "Bootstrap prediction intervals for SETAR models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 320-332, April.
    23. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working Papers 1210, University of Nevada, Las Vegas , Department of Economics.
    24. Huanxing Yang, 2010. "Information aggregation and investment cycles with strategic complementarity," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 43(2), pages 281-311, May.
    25. Pawel Milobedzki, 2010. "The Term Structure of the Polish Interbank Rates. A Note on the Symmetry of their Reversion to the Mean," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 10, pages 81-95.
    26. Zacharias Psaradakis & Fabio Spagnolo, 2005. "Forecast performance of nonlinear error-correction models with multiple regimes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(2), pages 119-138.
    27. G. Boero & E. Marrocu, 2001. "Evaluating non-linear models on point and interval forecasts: an application with exchange rate returns," Working Paper CRENoS 200110, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    28. Eraslan, Sercan & Nöller, Marvin, 2020. "Recession probabilities falling from the STARs," Discussion Papers 08/2020, Deutsche Bundesbank.
    29. Clementrs, Michael P. & Smith, Jeremy, 1997. "A Monte Carlo study of the forecasting performance of empirical SETAR models," Economic Research Papers 268734, University of Warwick - Department of Economics.
    30. Andrew Phiri, 2012. "Threshold effects and inflation persistence in South Africa," Journal of Financial Economic Policy, Emerald Group Publishing Limited, vol. 4(3), pages 247-269, July.
    31. Naraidoo, Ruthira & Paya, Ivan, 2012. "Forecasting monetary policy rules in South Africa," International Journal of Forecasting, Elsevier, vol. 28(2), pages 446-455.
    32. Clements Michael P., 2012. "Forecasting U.S. Output Growth with Non-Linear Models in the Presence of Data Uncertainty," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(1), pages 1-27, January.
    33. G. Boero & E. Marrocu, 2000. "La performance di modelli non lineari per i tassi di cambio: un'applicazione con dati a diversa frequenza," Working Paper CRENoS 200014, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    34. Yuzhi Cai, 2005. "A forecasting procedure for nonlinear autoregressive time series models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(5), pages 335-351.
    35. Franses,Philip Hans & Dijk,Dick van & Opschoor,Anne, 2014. "Time Series Models for Business and Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521817707, November.
    36. Crespo-Cuaresma, Jesus, 2000. "Forecasting European GDP Using Self-Exciting Threshold Autoregressive Models. A Warning," Economics Series 79, Institute for Advanced Studies.
    37. De Gooijer, Jan G. & Ray, Bonnie K., 2003. "Modeling vector nonlinear time series using POLYMARS," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 73-90, February.
    38. Manzan, S. & Zerom, D., 2005. "A Multi-Step Forecast Density," CeNDEF Working Papers 05-05, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    39. Ruthira Naraidoo & Ivan Paya, 2010. "Forecasting Monetary Rules in South Africa," Working Papers 201007, University of Pretoria, Department of Economics.
    40. 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.
    41. Dimitrios I. Vortelinos & Konstantinos Gkillas, 2018. "Intraday realised volatility forecasting and announcements," International Journal of Banking, Accounting and Finance, Inderscience Enterprises Ltd, vol. 9(1), pages 88-118.
    42. Li, Jing, 2011. "Bootstrap prediction intervals for SETAR models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 320-332.
    43. Milena Hoyos & Mario Galindo, 2011. "Comparación de los modelos SETAR y STAR para el índice de empleo industrial colombiano," Documentos de Trabajo, Escuela de Economía 8347, Universidad Nacional de Colombia, FCE, CID.
    44. Paulo M.M. Rodrigues & Nazarii Salish, 2011. "Modeling and Forecasting Interval Time Series with Threshold Models: An Application to S&P500 Index Returns," Working Papers w201128, Banco de Portugal, Economics and Research Department.
    45. Harvill, Jane L. & Ray, Bonnie K., 2005. "A note on multi-step forecasting with functional coefficient autoregressive models," International Journal of Forecasting, Elsevier, vol. 21(4), pages 717-727.
    46. Grabowski Daniel & Winker Peter & Staszewska-Bystrova Anna, 2017. "Generating prediction bands for path forecasts from SETAR models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(5), pages 1-18, December.
    47. Clements, Michael P. & Galvão, Ana Beatriz C., 2003. "Testing The Expectations Theory Of The Term Structure Of Interest Rates In Threshold Models," Macroeconomic Dynamics, Cambridge University Press, vol. 7(4), pages 567-585, September.
    48. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
    49. Massimo Guidolin & Carrie Fangzhou Na, 2007. "The economic and statistical value of forecast combinations under regime switching: an application to predictable U.S. returns," Working Papers 2006-059, Federal Reserve Bank of St. Louis.
    50. Leonardo Souza & Alvaro Veiga & Marcelo C. Medeiros, 2002. "Evaluating the performance of GARCH models using White´s Reality Check," Textos para discussão 453, Department of Economics PUC-Rio (Brazil).
    51. Holt, Matthew T. & Craig, Lee A., 2006. "AJAE Appendix: Nonlinear Dynamics and Structural Change in the U.S. Hog-Corn Ratio: A Time-Varying Star Approach," American Journal of Agricultural Economics APPENDICES, Agricultural and Applied Economics Association, vol. 88(1), pages 1-16, February.
    52. Dick van Dijk & Philip Hans Franses & Michael P. Clements & Jeremy Smith, 2003. "On SETAR non-linearity and forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(5), pages 359-375.
    53. Philippe Goulet Coulombe, 2020. "To Bag is to Prune," Papers 2008.07063, arXiv.org, revised Jun 2021.
    54. Philippe Goulet Coulombe, 2024. "The macroeconomy as a random forest," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 401-421, April.
    55. Clements, Michael P. & Galvao, Ana Beatriz, 2004. "A comparison of tests of nonlinear cointegration with application to the predictability of US interest rates using the term structure," International Journal of Forecasting, Elsevier, vol. 20(2), pages 219-236.

  45. Clements, Michael P. & Hendry, David F., 1996. "Multi-Step Estimation For Forecasting," Economic Research Papers 268696, University of Warwick - Department of Economics.

    Cited by:

    1. Clements, Michael P. & Smith, Jeremy, 1997. "The performance of alternative forecasting methods for SETAR models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 463-475, December.
    2. 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.
    3. Buncic, Daniel & Gisler, Katja I.M., 2016. "Global equity market volatility spillovers: A broader role for the United States," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1317-1339.
    4. Guillaume Chevillon, 2006. "Multi-step Forecasting in Unstable Economies: Robustness Issues in the Presence of Location Shifts," Economics Series Working Papers 257, University of Oxford, Department of Economics.
    5. Jari Hännikäinen, 2014. "Multi-step forecasting in the presence of breaks," Working Papers 1494, Tampere University, Faculty of Management and Business, Economics.
    6. 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.
    7. Marcellino, Massimliano, 2004. "Forecasting EMU macroeconomic variables," International Journal of Forecasting, Elsevier, vol. 20(2), pages 359-372.
    8. Janine Aron & John Muellbauer & Coen Pretorius, 2004. "A Framework for Forecasting the Components of the Consumer Price," Development and Comp Systems 0409054, University Library of Munich, Germany.
    9. Ericsson, Neil R., 2017. "Economic forecasting in theory and practice: An interview with David F. Hendry," International Journal of Forecasting, Elsevier, vol. 33(2), pages 523-542.
    10. Massimiliano Marcellino & James Stock & Mark Watson, 2005. "A Comparison of Direct and Iterated Multistep AR Methods for Forecasting Macroeconomic Time Series," Working Papers 285, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    11. Todd E. Clark & Michael W. McCracken, 2001. "Evaluating long-horizon forecasts," Research Working Paper RWP 01-14, Federal Reserve Bank of Kansas City.
    12. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    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. David Hendry & Guillaume Chevillon, 2004. "Non-Parametric Direct Multi-step Estimation for Forecasting Economic Processes," Economics Series Working Papers 196, University of Oxford, Department of Economics.
    15. Guillaume Chevillon, 2007. "Direct Multi‐Step Estimation And Forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 746-785, September.
    16. Proietti, Tommaso, 2011. "The Multistep Beveridge-Nelson Decomposition," Working Papers 09/2011, University of Sydney Business School, Discipline of Business Analytics.
    17. Buncic, Daniel & Piras, Gion Donat, 2014. "Heterogeneous Agents, the Financial Crisis and Exchange Rate Predictability," Economics Working Paper Series 1436, University of St. Gallen, School of Economics and Political Science, revised Oct 2015.
    18. Eliana González Molano & Luis Fernando Melo Velandia & Anderson Grajales Olarte, 2007. "Pronósticos directos de la inflación colombiana," Borradores de Economia 458, Banco de la Republica de Colombia.
    19. Hendry, David F., 2006. "Robustifying forecasts from equilibrium-correction systems," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 399-426.
    20. Osborn, Denise R. & Heravi, Saeed & Birchenhall, C. R., 1999. "Seasonal unit roots and forecasts of two-digit European industrial production," International Journal of Forecasting, Elsevier, vol. 15(1), pages 27-47, February.
    21. 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.
    22. 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.
    23. 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.
    24. Massimiliano Marcellino, "undated". "Instability and non-linearity in the EMU," Working Papers 211, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    25. Elena Ivona Dumitrescu & Peter Hansen, 2020. "How Should Parameter Estimation Be Tailored to the Objective?," Post-Print hal-03331109, HAL.
    26. Ralf Brüggemann & Helmut Lütkepohl & Massimiliano Marcellino, 2006. "Forecasting Euro-Area Variables with German Pre-EMU Data," SFB 649 Discussion Papers SFB649DP2006-065, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    27. 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.
    28. Michael ARTIS & Anindya BANERJEE & Massimiliano MARCELLINO, 2001. "Factor Forecasts for the UK," Economics Working Papers ECO2001/15, European University Institute.
    29. Janine Aron & John Muellbauer, 2013. "New Methods for Forecasting Inflation, Applied to the US," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(5), pages 637-661, October.
    30. Michael P. Clements & David F. Hendry, 2005. "Guest Editors’ Introduction: Information in Economic Forecasting," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 713-753, December.
    31. Marcellino, Massimiliano, 2006. "A Simple Benchmark for Forecasts of Growth and Inflation," CEPR Discussion Papers 6012, C.E.P.R. Discussion Papers.
    32. Proietti, Tommaso, 2008. "Direct and iterated multistep AR methods for difference stationary processes," MPRA Paper 10859, University Library of Munich, Germany.
    33. Marcellino, Massimiliano, 2002. "Forecast Pooling for Short Time Series of Macroeconomic Variables," CEPR Discussion Papers 3313, C.E.P.R. Discussion Papers.
    34. Heravi, Saeed & Osborn, Denise R. & Birchenhall, C. R., 2004. "Linear versus neural network forecasts for European industrial production series," International Journal of Forecasting, Elsevier, vol. 20(3), pages 435-446.
    35. Clements, Michael P., 2016. "Real-time factor model forecasting and the effects of instability," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 661-675.
    36. Kirstin Hubrich & David F. Hendry, 2005. "Forecasting Aggregates by Disaggregates," Computing in Economics and Finance 2005 270, Society for Computational Economics.
    37. Hendry, David F. & Hubrich, Kirstin, 2006. "Forecasting economic aggregates by disaggregates," Working Paper Series 589, European Central Bank.
    38. Janine Aron & John Muellbauer & Rachel Sebudde, 2015. "Inflation forecasting models for Uganda: is mobile money relevant?," CSAE Working Paper Series 2015-17, Centre for the Study of African Economies, University of Oxford.
    39. Hassani, Hossein & Webster, Allan & Silva, Emmanuel Sirimal & Heravi, Saeed, 2015. "Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis," Tourism Management, Elsevier, vol. 46(C), pages 322-335.
    40. Ahumada, H. & Cornejo, M., 2016. "Forecasting food prices: The case of corn, soybeans and wheat," International Journal of Forecasting, Elsevier, vol. 32(3), pages 838-848.
    41. Tommaso Proietti, 2005. "Forecasting and signal extraction with misspecified models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(8), pages 539-556.
    42. Janine Aron & John Muellbauer, 2008. "New methods for forecasting inflation and its sub-components: application to the USA," Economics Series Working Papers 406, University of Oxford, Department of Economics.
    43. Kauppi, Heikki & Virtanen, Timo, 2021. "Boosting nonlinear predictability of macroeconomic time series," International Journal of Forecasting, Elsevier, vol. 37(1), pages 151-170.
    44. Janine Aron & John Muellbauer & Coen Pretorius, 2004. "A Framework for Forecasting the Components of the Consumer Price Index: application to South Africa," CSAE Working Paper Series 2004-07, Centre for the Study of African Economies, University of Oxford.
    45. Eliana González Molano & Luis Fernando Melo Velnadia & Anderson Grajales Olarte, 2007. "Pronósticos directos de la inflación colombiana," Borradores de Economia 4247, Banco de la Republica.
    46. Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.
    47. Muellbauer, John & Aron, Janine, 2009. "Some Issues in Modeling and Forecasting Inflation in South Africa," CEPR Discussion Papers 7183, C.E.P.R. Discussion Papers.
    48. Trecroci, Carmine & Vega, Juan Luis, 2000. "The information content of M3 for future inflation," Working Paper Series 33, European Central Bank.
    49. Aron, Janine & Muellbauer, John, 2012. "Improving forecasting in an emerging economy, South Africa: Changing trends, long run restrictions and disaggregation," International Journal of Forecasting, Elsevier, vol. 28(2), pages 456-476.
    50. Chevillon, Guillaume, 2016. "Multistep forecasting in the presence of location shifts," International Journal of Forecasting, Elsevier, vol. 32(1), pages 121-137.
    51. 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.
    52. Janine Aron & John Muellbauer, 2008. "Multi-sector inflation forecasting - quarterly models for South Africa," CSAE Working Paper Series 2008-27, Centre for the Study of African Economies, University of Oxford.
    53. Buncic, Daniel & Moretto, Carlo, 2015. "Forecasting copper prices with dynamic averaging and selection models," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 1-38.
    54. Muellbauer, John & Aron, Janine, 2010. "Does aggregating forecasts by CPI component improve inflation forecast accuracy in South Africa?," CEPR Discussion Papers 7895, C.E.P.R. Discussion Papers.
    55. Janine Aron & John Muellbauer, 2000. "Inflation and output forecasts for South Africa: monetary transmission implications," CSAE Working Paper Series 2000-23, Centre for the Study of African Economies, University of Oxford.
    56. Clements, Michael P. & Hendry, David F., 1997. "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, Elsevier, vol. 13(3), pages 341-355, September.
    57. Janine Aron & John N. J. Muellbauer & Coen Pretorius, 2009. "A Stochastic Estimation Framework For Components Of The South African Consumer Price Index," South African Journal of Economics, Economic Society of South Africa, vol. 77(2), pages 282-313, June.
    58. Clements, Michael P. & Hendry, David F., 1998. "Forecasting economic processes," International Journal of Forecasting, Elsevier, vol. 14(1), pages 111-131, March.

  46. Clements, Michael P., 1996. "Evaluating the rationality of fixed-event forecasts," Economic Research Papers 268705, University of Warwick - Department of Economics.

    Cited by:

    1. Chia-Lin Chang & Bert de Bruijn & Philip Hans Franses & Michael McAleer, 2013. "Analyzing Fixed-event Forecast Revisions," Documentos de Trabajo del ICAE 2013-14, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico, revised Apr 2013.
    2. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    3. Carl Singleton & J. James Reade & Alsdair Brown, 2018. "Going with your Gut: The (In)accuracy of Forecast Revisions in a Football Score Prediction Game," Working Papers 2018-006, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    4. Timmermann, Allan & Patton, Andrew, 2007. "Learning in Real Time: Theory and Empirical Evidence from the Term Structure of Survey Forecasts," CEPR Discussion Papers 6526, C.E.P.R. Discussion Papers.
    5. Andrew J. Patton & Allan Timmermann, 2008. "The Resolution of Macroeconomic Uncertainty: Evidence from Survey Forecast," CREATES Research Papers 2008-54, Department of Economics and Business Economics, Aarhus University.
    6. Isengildina, Olga & Irwin, Scott H. & Good, Darrel L., 2004. "Does The Market Anticipate Smoothing In Usda Crop Production Forecasts?," 2004 Annual meeting, August 1-4, Denver, CO 20145, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).

  47. Clements, Michael P & Smith, Jeremy, 1996. "A Monte Carlo Study of the Forecasting Performance of Empirical Setar Models," The Warwick Economics Research Paper Series (TWERPS) 464, University of Warwick, Department of Economics.

    Cited by:

    1. Clements, Michael P. & Smith, Jeremy, 1997. "The performance of alternative forecasting methods for SETAR models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 463-475, December.
    2. McMillan, David G., 2007. "Non-linear forecasting of stock returns: Does volume help?," International Journal of Forecasting, Elsevier, vol. 23(1), pages 115-126.
    3. Terui, Nobuhiko & van Dijk, Herman K., 2002. "Combined forecasts from linear and nonlinear time series models," International Journal of Forecasting, Elsevier, vol. 18(3), pages 421-438.
    4. Paulo Rodrigues & Nazarii Salish, 2015. "Modeling and forecasting interval time series with threshold models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(1), pages 41-57, March.
    5. Simpson, Paul W & Osborn, Denise R & Sensier, Marianne, 2001. "Forecasting UK Industrial Production over the Business Cycle," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(6), pages 405-424, September.
    6. 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.
    7. Jack R. Rogers, 2013. "Monetary Transmission to UK Retail Mortgage Rates before and after August 2007," Discussion Papers 1307, University of Exeter, Department of Economics.
    8. Francisco Ledesma-Rodriguez & Manuel Navarro-Ibanez & Jorge Perez-Rodriguez & Simon Sosvilla-Rivero, 2005. "Assessing the credibility of a target zone: evidence from the EMS," Applied Economics, Taylor & Francis Journals, vol. 37(19), pages 2265-2287.
    9. 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.
    10. Oscar Claveria & Salvador Torra, 2013. "“Forecasting Business surveys indicators: neural networks vs. time series models”," IREA Working Papers 201320, University of Barcelona, Research Institute of Applied Economics, revised Nov 2013.
    11. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.
    12. Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents’ expectations. Different patterns of anticipation of the 2008 financial crisis”," AQR Working Papers 201508, University of Barcelona, Regional Quantitative Analysis Group, revised Mar 2015.
    13. Timo Teräsvirta & Dick van Dijk & Marcelo Cunha Medeiros, 2004. "Linear models, smooth transition autoregressions and neural networks for forecasting macroeconomic time series: A reexamination," Textos para discussão 485, Department of Economics PUC-Rio (Brazil).
    14. Uctum, Remzi, 2007. "Économétrie des modèles à changement de régimes : un essai de synthèse," L'Actualité Economique, Société Canadienne de Science Economique, vol. 83(4), pages 447-482, décembre.
    15. Kapetanios, G., 1999. "Threshold Models for Trended Time Series," Cambridge Working Papers in Economics 9905, Faculty of Economics, University of Cambridge.
    16. David G. McMillan, 2009. "Non-linear interest rate dynamics and forecasting: evidence for US and Australian interest rates," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 14(2), pages 139-155.
    17. Dominique Guegan, 2011. "Contagion Between the Financial Sphere and the Real Economy. Parametric and non Parametric Tools: A Comparison," PSE-Ecole d'économie de Paris (Postprint) halshs-00185373, HAL.
    18. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521770415, November.
    19. Lanne, Markku & Luoto, Jani & Saikkonen, Pentti, 2010. "Optimal Forecasting of Noncausal Autoregressive Time Series," MPRA Paper 23648, University Library of Munich, Germany.
    20. Hui Feng & Jia Liu, 2002. "A SETAR Model for Canadian GDP: Non-Linearities and Forecast Comparisons," Econometrics Working Papers 0206, Department of Economics, University of Victoria.
    21. Ahmad, Saad & Civelli, Andrea, 2016. "Globalization and inflation: A threshold investigation," Journal of Macroeconomics, Elsevier, vol. 48(C), pages 283-304.
    22. Franses, Ph.H.B.F. & van Dijk, D.J.C., 2001. "The forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production," Econometric Institute Research Papers EI 2001-14, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    23. Shiyi Chen & Wolfgang K. Härdle & Kiho Jeong, 2010. "Forecasting volatility with support vector machine-based GARCH model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(4), pages 406-433.
    24. G. Boero & E. Marrocu, 2002. "The performance of Setar Models: a regime conditional evaluation of point, interval and density forecasts," Working Paper CRENoS 200208, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    25. Li, Dongxin & Zhang, Li & Li, Lihong, 2023. "Forecasting stock volatility with economic policy uncertainty: A smooth transition GARCH-MIDAS model," International Review of Financial Analysis, Elsevier, vol. 88(C).
    26. Man-Wai Ng & Wai-Sum Chan, 2004. "Robustness of alternative non-linearity tests for SETAR models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(3), pages 215-231.
    27. Rossen Anja, 2016. "On the Predictive Content of Nonlinear Transformations of Lagged Autoregression Residuals and Time Series Observations," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 236(3), pages 389-409, May.
    28. Chunming Yuan, 2008. "Forecasting Exchange Rates: The Multi-State Markov-Switching Model with Smoothing," UMBC Economics Department Working Papers 09-115, UMBC Department of Economics, revised 01 Nov 2009.
    29. Michael Dueker & Martin Sola & Fabio Spagnolo, 2006. "Contemporaneous Threshold Autoregressive Models: Estimation, Testing and Forecasting," Department of Economics Working Papers 2006-04, Universidad Torcuato Di Tella.
    30. Rapach, David E. & Wohar, Mark E., 2006. "The out-of-sample forecasting performance of nonlinear models of real exchange rate behavior," International Journal of Forecasting, Elsevier, vol. 22(2), pages 341-361.
    31. Adusei Jumah & Robert M. Kunst, 2008. "Seasonal prediction of European cereal prices: good forecasts using bad models?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(5), pages 391-406.
    32. Nektarios Aslanidis, 2002. "Smooth Transition Regression Models in UK Stock Returns," Working Papers 0201, University of Crete, Department of Economics.
    33. Laurent Ferrara & Dominique Guegan, 2006. "Real-time detection of the business cycle using SETAR models," Post-Print halshs-00185372, HAL.
    34. Ahn, Eun S. & Lee, Jin Man, 2012. "The Performance Of Nonlinearity Tests On Asymmetric Nonlinear Time Series," The Journal of Economic Asymmetries, Elsevier, vol. 9(2), pages 11-44.
    35. Jennifer Castle & David Hendry, 2013. "Semi-automatic Non-linear Model selection," Economics Series Working Papers 654, University of Oxford, Department of Economics.
    36. Antoine Lejay & Paolo Pigato, 2020. "Maximum likelihood drift estimation for a threshold diffusion," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(3), pages 609-637, September.
    37. Li, Jing, 2011. "Bootstrap prediction intervals for SETAR models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 320-332, April.
    38. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working Papers 1210, University of Nevada, Las Vegas , Department of Economics.
    39. Chakradhara Panda & V. Narasimhan, 2006. "Predicting Stock Returns," South Asia Economic Journal, Institute of Policy Studies of Sri Lanka, vol. 7(2), pages 205-218, September.
    40. van Dijk, D.J.C. & Franses, Ph.H.B.F., 2003. "Selecting a Nonlinear Time Series Model using Weighted Tests of Equal Forecast Accuracy," Econometric Institute Research Papers EI 2003-10, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    41. Zacharias Psaradakis & Fabio Spagnolo, 2005. "Forecast performance of nonlinear error-correction models with multiple regimes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(2), pages 119-138.
    42. G. Boero & E. Marrocu, 2001. "Evaluating non-linear models on point and interval forecasts: an application with exchange rate returns," Working Paper CRENoS 200110, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    43. 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.
    44. Chunming Yuan, 2008. "The Exchange Rate and Macroeconomic Determinants: Time-Varying Transitional Dynamics," UMBC Economics Department Working Papers 09-114, UMBC Department of Economics, revised 01 Nov 2009.
    45. Clements Michael P., 2012. "Forecasting U.S. Output Growth with Non-Linear Models in the Presence of Data Uncertainty," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(1), pages 1-27, January.
    46. G. Boero & E. Marrocu, 2000. "La performance di modelli non lineari per i tassi di cambio: un'applicazione con dati a diversa frequenza," Working Paper CRENoS 200014, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    47. Clements, Michael P. & Smith, Jeremy, 2001. "Evaluating forecasts from SETAR models of exchange rates," Journal of International Money and Finance, Elsevier, vol. 20(1), pages 133-148, February.
    48. Dominique Guegan, 2011. "Contagion Between the Financial Sphere and the Real Economy. Parametric and non Parametric Tools: A Comparison," Post-Print halshs-00185373, HAL.
    49. Yuzhi Cai, 2005. "A forecasting procedure for nonlinear autoregressive time series models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(5), pages 335-351.
    50. Heravi, Saeed & Osborn, Denise R. & Birchenhall, C. R., 2004. "Linear versus neural network forecasts for European industrial production series," International Journal of Forecasting, Elsevier, vol. 20(3), pages 435-446.
    51. Mouratidis, Kostas, 2008. "Evaluating currency crises: A Bayesian Markov switching approach," Journal of Macroeconomics, Elsevier, vol. 30(4), pages 1688-1711, December.
    52. Guidolin, Massimo & Hyde, Stuart & McMillan, David & Ono, Sadayuki, 2009. "Non-linear predictability in stock and bond returns: When and where is it exploitable?," International Journal of Forecasting, Elsevier, vol. 25(2), pages 373-399.
    53. Shiyi Chen & Kiho Jeong & Wolfgang Härdle, 2008. "Support Vector Regression Based GARCH Model with Application to Forecasting Volatility of Financial Returns," SFB 649 Discussion Papers SFB649DP2008-014, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    54. M. Mallikarjuna & R. Prabhakara Rao, 2019. "Evaluation of forecasting methods from selected stock market returns," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-16, December.
    55. Andrea Cipollini & Kostas Mouratidis & Nicola Spagnolo, 2008. "Evaluating currency crises: the case of the European monetary system," Empirical Economics, Springer, vol. 35(1), pages 11-27, August.
    56. Dimitrios I. Vortelinos & Konstantinos Gkillas, 2018. "Intraday realised volatility forecasting and announcements," International Journal of Banking, Accounting and Finance, Inderscience Enterprises Ltd, vol. 9(1), pages 88-118.
    57. Claveria, Oscar & Pons, Ernest & Ramos, Raul, 2007. "Business and consumer expectations and macroeconomic forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 47-69.
    58. Li, Jing, 2011. "Bootstrap prediction intervals for SETAR models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 320-332.
    59. Perez-Rodriguez, Jorge V. & Torra, Salvador & Andrada-Felix, Julian, 2005. "STAR and ANN models: forecasting performance on the Spanish "Ibex-35" stock index," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 490-509, June.
    60. David Hendry, 2000. "A General Forecast-error Taxonomy," Econometric Society World Congress 2000 Contributed Papers 0608, Econometric Society.
    61. Florian Huber, 2014. "Forecasting Exchange Rates using Bayesian Threshold Vector Autoregressions," Economics Bulletin, AccessEcon, vol. 34(3), pages 1687-1695.
    62. Gianna Boero & Emanuela Marrocu, 2005. "Evaluating non-linear models on point and interval forecasts: an application with exchange rates," Banca Nazionale del Lavoro Quarterly Review, Banca Nazionale del Lavoro, vol. 58(232), pages 91-120.
    63. Paulo M.M. Rodrigues & Nazarii Salish, 2011. "Modeling and Forecasting Interval Time Series with Threshold Models: An Application to S&P500 Index Returns," Working Papers w201128, Banco de Portugal, Economics and Research Department.
    64. Grabowski Daniel & Winker Peter & Staszewska-Bystrova Anna, 2017. "Generating prediction bands for path forecasts from SETAR models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(5), pages 1-18, December.
    65. Miquel Clar & Juan-Carlos Duque & Rosina Moreno, 2007. "Forecasting business and consumer surveys indicators-a time-series models competition," Applied Economics, Taylor & Francis Journals, vol. 39(20), pages 2565-2580.
    66. Arora Siddharth & Little Max A. & McSharry Patrick E., 2013. "Nonlinear and nonparametric modeling approaches for probabilistic forecasting of the US gross national product," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(4), pages 395-420, September.
    67. Gabreyohannes, Emmanuel, 2010. "A nonlinear approach to modelling the residential electricity consumption in Ethiopia," Energy Economics, Elsevier, vol. 32(3), pages 515-523, May.
    68. Nadir Ocal & Denise R. Osborn, 2000. "Business cycle non-linearities in UK consumption and production," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(1), pages 27-43.
    69. Adrian Cantemir Calin & Tiberiu Diaconescu & Oana – Cristina Popovici, 2014. "Nonlinear Models for Economic Forecasting Applications: An Evolutionary Discussion," Computational Methods in Social Sciences (CMSS), "Nicolae Titulescu" University of Bucharest, Faculty of Economic Sciences, vol. 2(1), pages 42-47, June.
    70. Manahov, Viktor & Hudson, Robert & Hoque, Hafiz, 2015. "Return predictability and the ‘wisdom of crowds’: Genetic Programming trading algorithms, the Marginal Trader Hypothesis and the Hayek Hypothesis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 37(C), pages 85-98.
    71. Guney, Selin, 2015. "An Analysis of the Pass-Through of Exchange Rates in Tropical Forest Product Markets: A Smooth Transition Approach," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205107, Agricultural and Applied Economics Association.
    72. Mercedes Alda & Luis Ferruz, 2012. "Linear and nonlinear financial time series: evidence in a sample of pension funds in Spain and the United Kingdom," Applied Economics Letters, Taylor & Francis Journals, vol. 19(18), pages 1933-1937, December.
    73. Dick van Dijk & Philip Hans Franses & Michael P. Clements & Jeremy Smith, 2003. "On SETAR non-linearity and forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(5), pages 359-375.
    74. Laurent Ferrara & Dominique Guegan, 2006. "Real-time detection of the business cycle using SETAR models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00185372, HAL.
    75. Hongxing Yao & Abdul Rashid Abdul Rahaman, 2018. "Efficient Market Hypothesis and the RMB-Dollar Rates: A Nonlinear Modeling of the Exchange Rate," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(2), pages 150-160, February.
    76. Clements, Michael P. & Galvao, Ana Beatriz, 2004. "A comparison of tests of nonlinear cointegration with application to the predictability of US interest rates using the term structure," International Journal of Forecasting, Elsevier, vol. 20(2), pages 219-236.

  48. Clements, M.P. & Hendry, D., 1992. "On the Limitations of Comparing Mean Square Forecast Errors," Economics Series Working Papers 99138, University of Oxford, Department of Economics.

    Cited by:

    1. Athanasopoulos, George & Guillen, Osmani Teixeira Carvalho & Issler, João Victor & Vahid, Farshid, 2011. "Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 713, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    2. Massimiliano Marcellino, "undated". "Further Results on MSFE Encompassing," Working Papers 143, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    3. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    4. 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.
    5. Ester Ruiz & Fernando Lorenzo, 1997. "Prediction with univariate time series models: The Iberia case," Documentos de Trabajo (working papers) 0298, Department of Economics - dECON.
    6. Osmani Teixeira de Carvalho Guillén & João Victor Issler & George Athanasopoulos, 2005. "Forecasting Accuracy and Estimation Uncertainty Using VAR Models with Short- and Long-Term Economic Restrictions: A Monte-Carlo Study," Monash Econometrics and Business Statistics Working Papers 15/05, Monash University, Department of Econometrics and Business Statistics.
    7. Gustavsson, Patrik & Nordström, Jonas, 1999. "The Impact of Seasonal Unit Roots and Vector ARMA Modeling on Forecasting Monthly Tourism Flows," Working Paper Series 150, Trade Union Institute for Economic Research, revised 01 Jul 2000.
    8. Christoffersen, Peter F & Diebold, Francis X, 1998. "Cointegration and Long-Horizon Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(4), pages 450-458, October.
    9. Tom Engsted & Niels Haldrup & Boriss Siliverstovs, 2004. "Long-run forecasting in multicointegrated systems," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(5), pages 315-335.
    10. Oscar Jorda & Massimiliano Marcellino, 2008. "Path Forecast Evaluation," Working Papers 131, University of California, Davis, Department of Economics.
    11. David F. Hendry, 2002. "Forecast Failure, Expectations Formation and the Lucas Critique," Annals of Economics and Statistics, GENES, issue 67-68, pages 21-40.
    12. George Athanasopoulos & Farshid Vahid, 2008. "A complete VARMA modelling methodology based on scalar components," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(3), pages 533-554, May.
    13. Francis X. Diebold & Todd A. Gunther & Anthony S. Tay, 1997. "Evaluating Density Forecasts," Center for Financial Institutions Working Papers 97-37, Wharton School Center for Financial Institutions, University of Pennsylvania.
    14. Neil R. Ericsson, 2000. "Predictable uncertainty in economic forecasting," International Finance Discussion Papers 695, Board of Governors of the Federal Reserve System (U.S.).
    15. Michael P. Clements & David I. Harvey, 2010. "Forecast encompassing tests and probability forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(6), pages 1028-1062.
    16. Francis X. Diebold & Jose A. Lopez, 1996. "Forecast Evaluation and Combination," NBER Technical Working Papers 0192, National Bureau of Economic Research, Inc.
    17. Duo Qin & Sophie van Huellen & Qing Chao Wang & Thanos Moraitis, 2022. "Algorithmic Modelling of Financial Conditions for Macro Predictive Purposes: Pilot Application to USA Data," Econometrics, MDPI, vol. 10(2), pages 1-22, April.
    18. Athanasopoulos, George & Vahid, Farshid, 2008. "VARMA versus VAR for Macroeconomic Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 237-252, April.
    19. Neil R. Ericsson, David F. Hendry & Kevin M. Prestiwch, "undated". "The UK Demand for Broad Money over the Long run," Economics Papers W29, Economics Group, Nuffield College, University of Oxford.
    20. Vahid, Farshid & Issler, Joao Victor, 2002. "The importance of common cyclical features in VAR analysis: a Monte-Carlo study," Journal of Econometrics, Elsevier, vol. 109(2), pages 341-363, August.
    21. Neil R. Ericsson, 2001. "Forecast uncertainty in economic modeling," International Finance Discussion Papers 697, Board of Governors of the Federal Reserve System (U.S.).
    22. Yin-Wong Cheung & Menzie D. Chinn, 1997. "Integration, Cointegration and the Forecast Consistency of Structural Exchange Rate Models," NBER Working Papers 5943, National Bureau of Economic Research, Inc.
    23. Richard G. Anderson & Dennis L. Hoffman & Robert H. Rasche, 2001. "A vector error correction forecasting model of the U.S. economy," Working Papers 1998-008, Federal Reserve Bank of St. Louis.
    24. Clive G. Bowsher & Roland Meeks, 2008. "The dynamics of economics functions: modelling and forecasting the yield curve," Working Papers 0804, Federal Reserve Bank of Dallas.
    25. Fichtner, Ferdinand & Rüffer, Rasmus & Schnatz, Bernd, 2009. "Leading indicators in a globalised world," Working Paper Series 1125, European Central Bank.
    26. George Athanasopoulos & D.S. Poskitt & Farshid Vahid, 2007. "Two canonical VARMA forms: Scalar component models vis-à-vis the Echelon form," Monash Econometrics and Business Statistics Working Papers 10/07, Monash University, Department of Econometrics and Business Statistics, revised May 2009.
    27. Wagatha, Matthias, 2007. "Integration, Kointegration und die Langzeitprognose von Kreditausfallzyklen [Integration, Cointegration and Long-Horizont Forecasting of Credit-Default-Cycles]," MPRA Paper 8602, University Library of Munich, Germany.
    28. G. Boero & E. Marrocu, 2000. "La performance di modelli non lineari per i tassi di cambio: un'applicazione con dati a diversa frequenza," Working Paper CRENoS 200014, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    29. David Hendry & Michael P. Clements, 2001. "Pooling of Forecasts," Economics Papers 2002-W9, Economics Group, Nuffield College, University of Oxford.
    30. Bruce Mizrach, 1996. "Forecast Comparison in L2," Departmental Working Papers 199524, Rutgers University, Department of Economics.
    31. Neri, Marcelo Côrtes & Soares, Wagner Lopes, 2008. "Turismo sustentável e alivio a pobreza: avaliação de impacto," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 689, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    32. Fabio Busetti & Juri Marcucci & Giovanni Veronese, 2009. "Comparing forecast accuracy: A Monte Carlo investigation," Temi di discussione (Economic working papers) 723, Bank of Italy, Economic Research and International Relations Area.
    33. 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.
    34. David F. Hendry & Michael P. Clements, 1994. "Can Econometrics Improve Economic Forecasting?," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 130(III), pages 267-298, September.
    35. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    36. Norman Swanson & Oleg Korenok, 2006. "The Incremental Predictive Information Associated with Using Theoretical New Keynesian DSGE Models Versus Simple Linear Alternatives," Departmental Working Papers 200615, Rutgers University, Department of Economics.
    37. G. Boero & E. Marrocu, 1999. "Modelli non lineari per i tassi di cambio: un confronto previsivo," Working Paper CRENoS 199914, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    38. Richard Paap & Philip Hans Franses & Marco Van Der Leij, 2002. "Modelling and forecasting level shifts in absolute returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 601-616.
    39. Jennifer Castle & David Hendry, 2007. "Forecasting UK Inflation: the Roles of Structural Breaks and Time Disaggregation," Economics Series Working Papers 309, University of Oxford, Department of Economics.
    40. JS Armstrong & Robert Fildes, 2004. "Correspondence On the Selection of Error Measures for Comparisons Among Forecasting Methods," General Economics and Teaching 0412002, University Library of Munich, Germany.

  49. Clements, M.P. & Hendry, D.F., 1992. "Forecasting in Cointegrated Systems," Economics Series Working Papers 99139, University of Oxford, Department of Economics.

    Cited by:

    1. Igor Masten & Massimiliano Marcellino & Anindya Banerjeey, 2009. "Forecasting with Factor-augmented Error Correction Models," RSCAS Working Papers 2009/32, European University Institute.
    2. Athanasopoulos, George & Guillen, Osmani Teixeira Carvalho & Issler, João Victor & Vahid, Farshid, 2011. "Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 713, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    3. Berument, Hakan & Dogan, Nukhet & Tansel, Aysit, 2008. "Macroeconomic Policy and Unemployment by Economic Activity: Evidence from Turkey," IZA Discussion Papers 3461, Institute of Labor Economics (IZA).
    4. Osmani Teixeira de Carvalho Guillén & João Victor Issler & George Athanasopoulos, 2005. "Forecasting Accuracy and Estimation Uncertainty Using VAR Models with Short- and Long-Term Economic Restrictions: A Monte-Carlo Study," Monash Econometrics and Business Statistics Working Papers 15/05, Monash University, Department of Econometrics and Business Statistics.
    5. Christoffersen, Peter F & Diebold, Francis X, 1998. "Cointegration and Long-Horizon Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(4), pages 450-458, October.
    6. Tom Engsted & Niels Haldrup & Boriss Siliverstovs, 2004. "Long-run forecasting in multicointegrated systems," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(5), pages 315-335.
    7. BRATU SIMIONESCU, Mihaela, 2012. "Two Quantitative Forecasting Methods For Macroeconomic Indicators In Czech Republic," Annals of Spiru Haret University, Economic Series, Universitatea Spiru Haret, vol. 3(1), pages 71-87.
    8. Maghyereh, A., 2004. "Oil Price Shocks and Emerging Stock Markets: A Generalized VAR Approach," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 1(2), pages 27-40.
    9. Vahid, Farshid & Issler, Joao Victor, 2002. "The importance of common cyclical features in VAR analysis: a Monte-Carlo study," Journal of Econometrics, Elsevier, vol. 109(2), pages 341-363, August.
    10. Fichtner, Ferdinand & Rüffer, Rasmus & Schnatz, Bernd, 2009. "Leading indicators in a globalised world," Working Paper Series 1125, European Central Bank.
    11. Wagatha, Matthias, 2007. "Integration, Kointegration und die Langzeitprognose von Kreditausfallzyklen [Integration, Cointegration and Long-Horizont Forecasting of Credit-Default-Cycles]," MPRA Paper 8602, University Library of Munich, Germany.
    12. G. Boero & E. Marrocu, 2000. "La performance di modelli non lineari per i tassi di cambio: un'applicazione con dati a diversa frequenza," Working Paper CRENoS 200014, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    13. Friedrich Fritzer & Gabriel Moser & Johann Scharler, 2002. "Forecasting Austrian HICP and its Components using VAR and ARIMA Models," Working Papers 73, Oesterreichische Nationalbank (Austrian Central Bank).
    14. Rebecca A Emerson & David Hendry, 1994. "An evaluation of forecasting using leading indicators," Economics Papers 5., Economics Group, Nuffield College, University of Oxford.
    15. Neri, Marcelo Côrtes & Soares, Wagner Lopes, 2008. "Turismo sustentável e alivio a pobreza: avaliação de impacto," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 689, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    16. David Hendry, 2000. "A General Forecast-error Taxonomy," Econometric Society World Congress 2000 Contributed Papers 0608, Econometric Society.
    17. David F. Hendry & Michael P. Clements, 1994. "Can Econometrics Improve Economic Forecasting?," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 130(III), pages 267-298, September.
    18. Darius Hinz & Camille Logeay, 2006. "Forecasting Employment for Germany," IMK Working Paper 01-2006, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute, revised Jan 2006.
    19. G. Boero & E. Marrocu, 1999. "Modelli non lineari per i tassi di cambio: un confronto previsivo," Working Paper CRENoS 199914, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    20. John Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Staff Working Papers 07-1, Bank of Canada.

  50. Clements, M.P., 1989. "The Estimation And Testing Of Cointegrating Vectors: A Survey Of Recent Approaches And An Application To The U.K. Non-Durable Consumption Function," Economics Series Working Papers 9979, University of Oxford, Department of Economics.

    Cited by:

    1. Jenny Wilkinson, 1992. "Explaining Australia's Imports: 1974–1989," The Economic Record, The Economic Society of Australia, vol. 68(2), pages 151-164, June.
    2. David W.R. Gruen & Jenny Wilkinson, 1991. "Australia’s Real Exchange Rate – Is it Explained by the Terms of Trade or by Real Interest Differentials?," RBA Research Discussion Papers rdp9108, Reserve Bank of Australia.
    3. Masih, Abul M. M. & Masih, Rumi, 1997. "On the temporal causal relationship between energy consumption, real income, and prices: Some new evidence from Asian-energy dependent NICs Based on a multivariate cointegration/vector error-correctio," Journal of Policy Modeling, Elsevier, vol. 19(4), pages 417-440, August.
    4. Masih, Abul M. M. & Masih, Rumi, 1996. "Energy consumption, real income and temporal causality: results from a multi-country study based on cointegration and error-correction modelling techniques," Energy Economics, Elsevier, vol. 18(3), pages 165-183, July.
    5. Hurn, A Stan & Moody, Terry & Muscatelli, V Anton, 1995. "The Term Structure of Interest Rates in the London Interbank Market," Oxford Economic Papers, Oxford University Press, vol. 47(3), pages 419-436, July.

Articles

  1. 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.
    See citations under working paper version above.
  2. 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.

    Cited by:

    1. Michael Pfarrhofer, 2024. "Forecasts with Bayesian vector autoregressions under real time conditions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 771-801, April.

  3. Michael P. Clements, 2022. "Individual forecaster perceptions of the persistence of shocks to GDP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 640-656, April.
    See citations under working paper version above.
  4. Michael P. Clements, 2022. "Forecaster Efficiency, Accuracy, and Disagreement: Evidence Using Individual‐Level Survey Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(2-3), pages 537-568, March.

    Cited by:

    1. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.

  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.
    See citations under working paper version above.
  6. Clements, Michael P. & Galvão, Ana Beatriz, 2021. "Measuring the effects of expectations shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 124(C).
    See citations under working paper version above.
  7. Clements, Michael P., 2021. "Rounding behaviour of professional macro-forecasters," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1614-1631.

    Cited by:

    1. Camille Cornand & Paul Hubert, 2022. "Information frictions in inflation expectations among five types of economic agents," Post-Print hal-03468918, HAL.
    2. Dovern, Jonas & Glas, Alexander & Kenny, Geoff, 2023. "Testing for differences in survey-based density expectations: a compositional data approach," Working Paper Series 2791, European Central Bank.
    3. Zhao, Yongchen, 2023. "Internal consistency of household inflation expectations: Point forecasts vs. density forecasts," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1713-1735.
    4. Cornand Camille & Hubert Paul, 2022. "Information Frictions Across Various Types of Inflation Expectations," Working papers 873, Banque de France.

  8. Clements, Michael P., 2021. "Do survey joiners and leavers differ from regular participants? The US SPF GDP growth and inflation forecasts," International Journal of Forecasting, Elsevier, vol. 37(2), pages 634-646.
    See citations under working paper version above.
  9. Michael P. Clements, 2020. "Are Some Forecasters’ Probability Assessments of Macro Variables Better Than Those of Others?," Econometrics, MDPI, vol. 8(2), pages 1-16, May.

    Cited by:

    1. An, Zidong & Zheng, Xinye, 2023. "Diligent forecasters can make accurate predictions despite disagreeing with the consensus," Economic Modelling, Elsevier, vol. 125(C).
    2. Klein, Tony, 2021. "Agree to Disagree? Predictions of U.S. Nonfarm Payroll Changes between 2008 and 2020 and the Impact of the COVID19 Labor Shock," QBS Working Paper Series 2021/07, Queen's University Belfast, Queen's Business School.
    3. Ulrich Hounyo & Kajal Lahiri, 2023. "Are Some Forecasters Really Better than Others? A Note," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 55(2-3), pages 577-593, March.
    4. Clements, Michael P., 2021. "Rounding behaviour of professional macro-forecasters," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1614-1631.
    5. Michael P. Clements, 2020. "Do Survey Joiners and Leavers Differ from Regular Participants? The US SPF GDP Growth and Inflation Forecasts," ICMA Centre Discussion Papers in Finance icma-dp2020-01, Henley Business School, University of Reading.
    6. Klein, Tony, 2022. "Agree to disagree? Predictions of U.S. nonfarm payroll changes between 2008 and 2020 and the impact of the COVID19 labor shock," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 264-286.

  10. Clements, Michael P. & Reade, J. James, 2020. "Forecasting and forecast narratives: The Bank of England Inflation Reports," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1488-1500.

    Cited by:

    1. Foltas, Alexander, 2023. "Quantifying priorities in business cycle reports: Analysis of recurring textual patterns around peaks and troughs," Working Papers 44, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    2. Jacob T. Jones & Tara M. Sinclair & Herman O. Stekler, 2018. "A Textual Analysis of the Bank of England Growth Forecasts," Working Papers 2018-005, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting, revised May 2019.
    3. Niţoi, Mihai & Pochea, Maria-Miruna & Radu, Ştefan-Constantin, 2023. "Unveiling the sentiment behind central bank narratives: A novel deep learning index," Journal of Behavioral and Experimental Finance, Elsevier, vol. 38(C).
    4. Müller, Karsten, 2020. "German forecasters' narratives: How informative are German business cycle forecast reports?," Working Papers 23, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    5. Sharpe, Steven A. & Sinha, Nitish R. & Hollrah, Christopher A., 2023. "The power of narrative sentiment in economic forecasts," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1097-1121.
    6. Jennifer Castle & David Hendry, 2016. "Policy Analysis, Forediction, and Forecast Failure," Economics Series Working Papers 809, University of Oxford, Department of Economics.
    7. Rambaccussing, Dooruj & Kwiatkowski, Andrzej, 2020. "Forecasting with news sentiment: Evidence with UK newspapers," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1501-1516.
    8. Simionescu, Mihaela, 2022. "Econometrics of sentiments- sentometrics and machine learning: The improvement of inflation predictions in Romania using sentiment analysis," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    9. Jennifer L. Castle & David F. Hendry & Andrew B. Martinez, 2017. "Evaluating Forecasts, Narratives and Policy Using a Test of Invariance," Econometrics, MDPI, vol. 5(3), pages 1-27, September.
    10. Karsten Müller, 2022. "German forecasters’ narratives: How informative are German business cycle forecast reports?," Empirical Economics, Springer, vol. 62(5), pages 2373-2415, May.
    11. Timo Dimitriadis & Xiaochun Liu & Julie Schnaitmann, 2020. "Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary," Papers 2009.07341, arXiv.org.
    12. Ilias Filippou & James Mitchell & My T. Nguyen, 2023. "The FOMC versus the Staff: Do Policymakers Add Value in Their Tales?," Working Papers 23-20, Federal Reserve Bank of Cleveland.
    13. Dooruj Rambaccussing & Craig Menzies & Andrzej Kwiatkowski, 2022. "Look who’s Talking: Individual Committee members’ impact on inflation expectations," Dundee Discussion Papers in Economics 305, Economic Studies, University of Dundee.

  11. Clements, Michael P., 2019. "Do forecasters target first or later releases of national accounts data?," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1240-1249.
    See citations under working paper version above.
  12. Clements, Michael P., 2018. "Are macroeconomic density forecasts informative?," International Journal of Forecasting, Elsevier, vol. 34(2), pages 181-198.
    See citations under working paper version above.
  13. Michael P. Clements, 2018. "Do Macroforecasters Herd?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(2-3), pages 265-292, March.

    Cited by:

    1. Robert Rich & Joseph Tracy, 2021. "A Closer Look at the Behavior of Uncertainty and Disagreement: Micro Evidence from the Euro Area," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(1), pages 233-253, February.
    2. Boskabadi, Elahe, 2022. "Economic policy uncertainty and forecast bias in the survey of professional forecasters," MPRA Paper 115081, University Library of Munich, Germany.
    3. Levis, Mario & Muradoğlu, Yaz Gulnur & Vasileva, Kristina, 2023. "Herding in foreign direct investment," International Review of Financial Analysis, Elsevier, vol. 86(C).
    4. Meade, Nigel & Driver, Ciaran, 2023. "Differing behaviours of forecasters of UK GDP growth," International Journal of Forecasting, Elsevier, vol. 39(2), pages 772-790.
    5. Goldstein, Nathan & Zilberfarb, Ben-Zion, 2021. "Do forecasters really care about consensus?," Economic Modelling, Elsevier, vol. 100(C).
    6. Keppo, Jussi & Satopää, Ville A., 2024. "Bayesian herd detection for dynamic data," International Journal of Forecasting, Elsevier, vol. 40(1), pages 285-301.
    7. Yoichi Tsuchiya, 2021. "Crises, market shocks, and herding behavior in stock price forecasts," Empirical Economics, Springer, vol. 61(2), pages 919-945, August.

  14. Meng, Yijun & Clements, Michael P. & Padgett, Carol, 2018. "Independent directors, information costs and foreign ownership in Chinese companies," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 53(C), pages 139-157.

    Cited by:

    1. Haozhe Han, 2023. "Does increasing the QFII quota promote Chinese institutional investors to drive ESG?," Asia-Pacific Journal of Accounting & Economics, Taylor & Francis Journals, vol. 30(6), pages 1627-1643, November.
    2. Hsueh-Li Huang & Lien-Wen Liang & Hai-Yen Chang & Hsiu-Yuan Hsu, 2021. "The Influence of Earnings Management and Board Characteristics on Company Efficiency," Sustainability, MDPI, vol. 13(21), pages 1-18, October.
    3. Ullah, Subhan & Agyei-Boapeah, Henry & Kim, Ja Ryong & Nasim, Asma, 2022. "Does national culture matter for environmental innovation? A study of emerging economies," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    4. Elnahass, Marwa & Salama, Aly & Trinh, Vu Quang, 2022. "Firm valuations and board compensation: Evidence from alternative banking models," Global Finance Journal, Elsevier, vol. 51(C).
    5. Anh‐Tuan Doan & Anh‐Tuan Le & Quan Tran, 2020. "Economic uncertainty, ownership structure and small and medium enterprises performance," Australian Economic Papers, Wiley Blackwell, vol. 59(2), pages 102-137, June.
    6. Marwa Elnahass & Kamil Omoteso & Aly Salama & Vu Quang Trinh, 2020. "Differential market valuations of board busyness across alternative banking models," Review of Quantitative Finance and Accounting, Springer, vol. 55(1), pages 201-238, July.
    7. Iwasaki, Ichiro & Ma, Xinxin & Mizobata, Satoshi, 2022. "Ownership structure and firm performance in emerging markets: A comparative meta-analysis of East European EU member states, Russia and China," Economic Systems, Elsevier, vol. 46(2).
    8. Vu Quang Trinh & Marwa Elnahass & Aly Salama, 2021. "Board busyness and new insights into alternative bank dividends models," Review of Quantitative Finance and Accounting, Springer, vol. 56(4), pages 1289-1328, May.
    9. Quang Trinh, Vu & Elnahass, Marwa & Duong Cao, Ngan, 2021. "The value relevance of bank cash Holdings: The moderating effect of board busyness," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 73(C).
    10. Sujuan Xie & Yue Xu & Yamin Zeng & Junsheng Zhang, 2019. "Ultimate parent board reform and corporate overinvestment: a quasi‐natural experiment study," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(5), pages 1469-1501, March.

  15. Michael P. Clements, 2017. "Assessing Macro Uncertainty in Real-Time When Data Are Subject To Revision," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 420-433, July.
    See citations under working paper version above.
  16. Michael P. Clements & Ana Beatriz Galvão, 2017. "Predicting Early Data Revisions to U.S. GDP and the Effects of Releases on Equity Markets," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 389-406, July.

    Cited by:

    1. 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.
    2. van der Bles, Anne Marthe & van der Liden, Sander & Freeman, Alessandra L. J. & Mitchell, James & Galvao, Ana Beatriz & Spiegelhalter, David J., 2019. "Communicating uncertainty about facts, numbers, and science," EMF Research Papers 22, Economic Modelling and Forecasting Group.
    3. Clements, Michael P. & Galvao, Ana Beatriz, 2019. "Measuring the Effects of Expectations Shocks," EMF Research Papers 31, Economic Modelling and Forecasting Group.
    4. Michael Clements, 2017. "Do forecasters target first or later releases of national accounts data?," ICMA Centre Discussion Papers in Finance icma-dp2017-03, Henley Business School, University of Reading.
    5. 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.
    6. Gatti, Roberta & Lederman, Daniel & Islam, Asif M. & Nguyen, Ha & Lotfi, Rana & Emam Mousa, Mennatallah, 2024. "Data transparency and GDP growth forecast errors," Journal of International Money and Finance, Elsevier, vol. 140(C).
    7. Tommaso Proietti & Alessandro Giovannelli, 2021. "Nowcasting monthly GDP with big data: A model averaging approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(2), pages 683-706, April.
    8. Galvao, Ana Beatriz & Mitchell, James & Runge, Johnny, 2019. "Communicating Data Uncertainty: Experimental Evidence for U.K. GDP," EMF Research Papers 30, Economic Modelling and Forecasting Group.
    9. Ana Beatriz Galvão & James Mitchell, 2023. "Real‐Time Perceptions of Historical GDP Data Uncertainty," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 457-481, June.
    10. Funashima, Yoshito & Iizuka, Nobuo & Ohtsuka, Yoshihiro, 2020. "GDP announcements and stock prices," Journal of Economics and Business, Elsevier, vol. 108(C).
    11. 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.
    12. Ederington, Louis & Guan, Wei & Yang, Lisa (Zongfei), 2019. "The impact of the U.S. employment report on exchange rates," Journal of International Money and Finance, Elsevier, vol. 90(C), pages 257-267.
    13. Sayag, Doron & Ben-hur, Dano & Pfeffermann, Danny, 2022. "Reducing revisions in hedonic house price indices by the use of nowcasts," International Journal of Forecasting, Elsevier, vol. 38(1), pages 253-266.
    14. Alex Minne & Marc Francke & David Geltner & Robert White, 2020. "Using Revisions as a Measure of Price Index Quality in Repeat-Sales Models," The Journal of Real Estate Finance and Economics, Springer, vol. 60(4), pages 514-553, May.

  17. 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.

    Cited by:

    1. Kajal Lahiri & Huaming Peng & Xuguang Simon Sheng, 2021. "Measuring Uncertainty of a Combined Forecast and Some Tests for Forecaster Heterogeneity," Working Papers 2021-005, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    2. Michael Clements, 2016. "Are Macroeconomic Density Forecasts Informative?," ICMA Centre Discussion Papers in Finance icma-dp2016-02, Henley Business School, University of Reading.
    3. Knüppel, Malte & Schultefrankenfeld, Guido, 2019. "Assessing the uncertainty in central banks’ inflation outlooks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1748-1769.
    4. 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.
    5. Liu, Yang & Sheng, Xuguang Simon, 2019. "The measurement and transmission of macroeconomic uncertainty: Evidence from the U.S. and BRIC countries," International Journal of Forecasting, Elsevier, vol. 35(3), pages 967-979.
    6. Oscar Claveria, 2021. "Forecasting with Business and Consumer Survey Data," Forecasting, MDPI, vol. 3(1), pages 1-22, February.
    7. 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.
    8. 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.
    9. Huang, Rong & Pilbeam, Keith & Pouliot, William, 2022. "Are macroeconomic forecasters optimists or pessimists? A reassessment of survey based forecasts," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 706-724.
    10. Oscar Claveria, 2020. "Business and consumer uncertainty in the face of the pandemic: A sector analysis in European countries," Papers 2012.02091, arXiv.org.
    11. 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.
    12. Oscar Claveria, 2021. "Disagreement on expectations: firms versus consumers," SN Business & Economics, Springer, vol. 1(12), pages 1-23, December.
    13. Lee, Hangyu & Kim, Tae Bong, 2023. "The effectiveness of labor market indicators for conducting monetary policy: Evidence from the Korean economy," Economic Modelling, Elsevier, vol. 118(C).
    14. Todd E. Clark & Gergely Ganics & Elmar Mertens, 2022. "What is the Predictive Value of SPF Point and Density Forecasts?," Working Papers 22-37, Federal Reserve Bank of Cleveland.
    15. Michael P. Clements, 2020. "Do Survey Joiners and Leavers Differ from Regular Participants? The US SPF GDP Growth and Inflation Forecasts," ICMA Centre Discussion Papers in Finance icma-dp2020-01, Henley Business School, University of Reading.
    16. Oscar Claveria & Petar Sorić, 2023. "Labour market uncertainty after the irruption of COVID-19," Empirical Economics, Springer, vol. 64(4), pages 1897-1945, April.
    17. Oscar Claveria, 2020. "Measuring and assessing economic uncertainty," IREA Working Papers 202011, University of Barcelona, Research Institute of Applied Economics, revised Jul 2020.

  18. Clements, Michael P., 2016. "Long-run restrictions and survey forecasts of output, consumption and investment," International Journal of Forecasting, Elsevier, vol. 32(3), pages 614-628.
    See citations under working paper version above.
  19. 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.
    See citations under working paper version above.
  20. Clements, Michael P., 2016. "Real-time factor model forecasting and the effects of instability," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 661-675.
    See citations under working paper version above.
  21. 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.

    Cited by:

    1. Anesti, Nikoleta & Galvão, Ana & Miranda-Agrippino, Silvia, 2018. "Uncertain Kingdom: nowcasting GDP and its revisions," Bank of England working papers 764, Bank of England, revised 31 Jan 2020.
    2. 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.
    3. Amir-Ahmadi, Pooyan & Matthes, Christian & Wang, Mu-Chun, 2017. "Measurement errors and monetary policy: Then and now," Journal of Economic Dynamics and Control, Elsevier, vol. 79(C), pages 66-78.
    4. M. Mogliani & T. Ferrière, 2016. "Rationality of announcements, business cycle asymmetry, and predictability of revisions. The case of French GDP," Working papers 600, Banque de France.
    5. 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. 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.
    7. Asimakopoulos, Stylianos & Lalik, Magdalena & Paredes, Joan & Salvado García, José, 2023. "GDP revisions are not cool: the impact of statistical agencies’ trade-off," Working Paper Series 2857, European Central Bank.
    8. 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.

  22. Michael P. Clements, 2015. "Do US Macroeconomic Forecasters Exaggerate their Differences?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(8), pages 649-660, December.
    See citations under working paper version above.
  23. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2015. "Robust approaches to forecasting," International Journal of Forecasting, Elsevier, vol. 31(1), pages 99-112.
    See citations under working paper version above.
  24. Michael P. Clements, 2015. "Are Professional Macroeconomic Forecasters Able To Do Better Than Forecasting Trends?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(2-3), pages 349-382, March.

    Cited by:

    1. Michael Clements, 2016. "Are Macroeconomic Density Forecasts Informative?," ICMA Centre Discussion Papers in Finance icma-dp2016-02, Henley Business School, University of Reading.
    2. Emmanuel C. Mamatzakis & Mike G. Tsionas, 2020. "Revealing forecaster's preferences: A Bayesian multivariate loss function approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 412-437, April.
    3. Ye, Wuyi & Guo, Ranran & Deschamps, Bruno & Jiang, Ying & Liu, Xiaoquan, 2021. "Macroeconomic forecasts and commodity futures volatility," Economic Modelling, Elsevier, vol. 94(C), pages 981-994.
    4. Benjamin Beckers & Konstantin A. Kholodilin & Dirk Ulbricht, 2017. "Reading between the Lines: Using Media to Improve German Inflation Forecasts," Discussion Papers of DIW Berlin 1665, DIW Berlin, German Institute for Economic Research.
    5. Michael Clements, 2017. "Do forecasters target first or later releases of national accounts data?," ICMA Centre Discussion Papers in Finance icma-dp2017-03, Henley Business School, University of Reading.
    6. Danilo Cascaldi-Garcia, 2022. "Forecast Revisions as Instruments for News Shocks," International Finance Discussion Papers 1341, Board of Governors of the Federal Reserve System (U.S.).
    7. Didier Nibbering & Richard Paap & Michel van der Wel, 2015. "What Do Professional Forecasters Actually Predict?," Tinbergen Institute Discussion Papers 15-095/III, Tinbergen Institute, revised 13 Oct 2017.
    8. Michael P. Clements, 2014. "Long-Run Restrictions and Survey Forecasts of Output, Consumption and Investment," ICMA Centre Discussion Papers in Finance icma-dp2014-02, Henley Business School, University of Reading.
    9. Clements, Michael P., 2021. "Rounding behaviour of professional macro-forecasters," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1614-1631.
    10. Bespalova, Olga, 2018. "Forecast Evaluation in Macroeconomics and International Finance. Ph.D. thesis, George Washington University, Washington, DC, USA," MPRA Paper 117706, University Library of Munich, Germany.
    11. Clements, Michael P. & Reade, J. James, 2020. "Forecasting and forecast narratives: The Bank of England Inflation Reports," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1488-1500.

  25. Clements, Michael P., 2014. "Probability distributions or point predictions? Survey forecasts of US output growth and inflation," International Journal of Forecasting, Elsevier, vol. 30(1), pages 99-117.
    See citations under working paper version above.
  26. Michael P. Clements, 2014. "Forecast Uncertainty- Ex Ante and Ex Post : U.S. Inflation and Output Growth," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 206-216, April.

    Cited by:

    1. Krüger, Fabian & Nolte, Ingmar, 2016. "Disagreement versus uncertainty: Evidence from distribution forecasts," Journal of Banking & Finance, Elsevier, vol. 72(S), pages 172-186.
    2. Wojciech CHAREMZA & Carlos DÍAZ & Svetlana MAKAROVA, 2019. "Conditional Term Structure of Inflation Forecast Uncertainty: The Copula Approach," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 5-18, March.
    3. Gergely Ganics & Barbara Rossi & Tatevik Sekhposyan, 2019. "From fixed-event to fixed-horizon density forecasts: Obtaining measures of multi-horizon uncertainty from survey density forecasts," Economics Working Papers 1689, Department of Economics and Business, Universitat Pompeu Fabra.
    4. Kajal Lahiri & Huaming Peng & Xuguang Simon Sheng, 2021. "Measuring Uncertainty of a Combined Forecast and Some Tests for Forecaster Heterogeneity," Working Papers 2021-005, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    5. Bańbura, Marta & Leiva-Leon, Danilo & Menz, Jan-Oliver, 2021. "Do inflation expectations improve model-based inflation forecasts?," Working Paper Series 2604, European Central Bank.
    6. Michael Clements, 2016. "Are Macroeconomic Density Forecasts Informative?," ICMA Centre Discussion Papers in Finance icma-dp2016-02, Henley Business School, University of Reading.
    7. 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.
    8. Michael P. Clements, 2022. "Individual forecaster perceptions of the persistence of shocks to GDP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 640-656, April.
    9. Knüppel, Malte & Schultefrankenfeld, Guido, 2019. "Assessing the uncertainty in central banks’ inflation outlooks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1748-1769.
    10. Glas, Alexander, 2020. "Five dimensions of the uncertainty–disagreement linkage," International Journal of Forecasting, Elsevier, vol. 36(2), pages 607-627.
    11. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2023. "Out-of-sample tests for conditional quantile coverage an application to Growth-at-Risk," Journal of Econometrics, Elsevier, vol. 236(2).
    12. Simionescu, Mihaela, 2017. "Prediction intervals for inflation and unemployment rate in Romania. A Bayesian approach," GLO Discussion Paper Series 82, Global Labor Organization (GLO).
    13. 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.
    14. Manzan, Sebastiano, 2021. "Are professional forecasters Bayesian?," Journal of Economic Dynamics and Control, Elsevier, vol. 123(C).
    15. Robert Rich & Joseph Tracy, 2021. "A Closer Look at the Behavior of Uncertainty and Disagreement: Micro Evidence from the Euro Area," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 53(1), pages 233-253, February.
    16. Dovern, Jonas, 2023. "Eliciting expectation uncertainty from private households," Working Papers 38, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    17. Alexander Glas & Matthias Hartmann, 2022. "Uncertainty measures from partially rounded probabilistic forecast surveys," Quantitative Economics, Econometric Society, vol. 13(3), pages 979-1022, July.
    18. Ambrocio, Gene, 2020. "Inflationary household uncertainty shocks," Bank of Finland Research Discussion Papers 5/2020, Bank of Finland.
    19. Michael Clements, 2016. "Are Macro-Forecasters Essentially The Same? An Analysis of Disagreement, Accuracy and Efficiency," ICMA Centre Discussion Papers in Finance icma-dp2016-08, Henley Business School, University of Reading.
    20. Liu, Yang & Sheng, Xuguang Simon, 2019. "The measurement and transmission of macroeconomic uncertainty: Evidence from the U.S. and BRIC countries," International Journal of Forecasting, Elsevier, vol. 35(3), pages 967-979.
    21. Knüppel, Malte & Krüger, Fabian & Pohle, Marc-Oliver, 2022. "Score-based calibration testing for multivariate forecast distributions," Discussion Papers 50/2022, Deutsche Bundesbank.
    22. Wojciech Charemza & Carlos Diaz Vela & Svetlana Makarova, 2013. "Too many skew normal distributions? The practitioner’s perspective," Discussion Papers in Economics 13/07, Division of Economics, School of Business, University of Leicester.
    23. Ana Beatriz Galvão & James Mitchell, 2019. "Measuring Data Uncertainty: An Application using the Bank of England's "Fan Charts" for Historical GDP Growth," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2019-08, Economic Statistics Centre of Excellence (ESCoE).
    24. Charemza, Wojciech, 2020. "Central banks' voting contest," MPRA Paper 101205, University Library of Munich, Germany.
    25. Krüger, Fabian & Pavlova, Lora, 2020. "Quantifying subjective uncertainty in survey expectations," Working Paper Series in Economics 139, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    26. Andrew B. Martinez, 2020. "Forecast Accuracy Matters for Hurricane Damages," Working Papers 2020-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    27. Michael P. Clements & Ana Beatriz Galvão, 2014. "Measuring Macroeconomic Uncertainty: US Inflation and Output Growth," ICMA Centre Discussion Papers in Finance icma-dp2014-04, Henley Business School, University of Reading.
    28. Rossi, Barbara & Ganics, Gergely & Sekhposyan, Tatevik, 2020. "From Fixed-event to Fixed-horizon Density Forecasts: Obtaining Measures of Multi-horizon Uncertainty from Survey Density Foreca," CEPR Discussion Papers 14267, C.E.P.R. Discussion Papers.
    29. Mawuli Segnon & Stelios Bekiros & Bernd Wilfling, 2018. "Forecasting Inflation Uncertainty in the G7 Countries," Econometrics, MDPI, vol. 6(2), pages 1-25, April.
    30. Todd E. Clark & Michael W. McCracken & Elmar Mertens, 2017. "Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors," Working Papers (Old Series) 1715, Federal Reserve Bank of Cleveland.
    31. Chen, Qiwei & Costantini, Mauro & Deschamps, Bruno, 2016. "How accurate are professional forecasts in Asia? Evidence from ten countries," International Journal of Forecasting, Elsevier, vol. 32(1), pages 154-167.
    32. Michael P. Clements, 2022. "Forecaster Efficiency, Accuracy, and Disagreement: Evidence Using Individual‐Level Survey Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(2-3), pages 537-568, March.
    33. Hartmann, Matthias & Herwartz, Helmut & Ulm, Maren, 2017. "A comparative assessment of alternative ex ante measures of inflation uncertainty," International Journal of Forecasting, Elsevier, vol. 33(1), pages 76-89.
    34. Knüppel, Malte & Krüger, Fabian, 2019. "Forecast uncertainty, disagreement, and the linear pool," Discussion Papers 28/2019, Deutsche Bundesbank.
    35. Huang, Rong & Pilbeam, Keith & Pouliot, William, 2022. "Are macroeconomic forecasters optimists or pessimists? A reassessment of survey based forecasts," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 706-724.
    36. Chini, Emilio Zanetti, 2023. "Can we estimate macroforecasters’ mis-behavior?," Journal of Economic Dynamics and Control, Elsevier, vol. 149(C).
    37. Ezgi O. Ozturk & Xuguang Simon Sheng, 2017. "Measuring Global and Country-Specific Uncertainty," IMF Working Papers 2017/219, International Monetary Fund.
    38. Svetlana Makarova, 2016. "ECB footprints on inflation forecast uncertainty," Bank of Estonia Working Papers wp2016-5, Bank of Estonia, revised 19 Jul 2016.
    39. Michael P. Clements, 2014. "Long-Run Restrictions and Survey Forecasts of Output, Consumption and Investment," ICMA Centre Discussion Papers in Finance icma-dp2014-02, Henley Business School, University of Reading.
    40. Wojciech Charemza & Carlos Diaz Vela & Svetlana Makarova, 2013. "Inflation fan charts, monetary policy and skew normal distribution," Discussion Papers in Economics 13/06, Division of Economics, School of Business, University of Leicester.
    41. Simón Sosvilla-Rivero & María del Carmen Ramos-Herrera, 2018. "Inflation, real economic growth and unemployment expectations: an empirical analysis based on the ECB survey of professional forecasters," Applied Economics, Taylor & Francis Journals, vol. 50(42), pages 4540-4555, September.
    42. Clements, Michael P., 2021. "Rounding behaviour of professional macro-forecasters," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1614-1631.
    43. Glas, Alexander & Hartmann, Matthias, 2016. "Inflation uncertainty, disagreement and monetary policy: Evidence from the ECB Survey of Professional Forecasters," Journal of Empirical Finance, Elsevier, vol. 39(PB), pages 215-228.
    44. Rahul Kapoor & Daniel Wilde, 2023. "Peering into a crystal ball: Forecasting behavior and industry foresight," Strategic Management Journal, Wiley Blackwell, vol. 44(3), pages 704-736, March.
    45. Mihaela SIMIONESCU, 2015. "The Evaluation of Global Accuracy of Romanian Inflation Rate Predictions Using Mahalanobis Distance," Management Dynamics in the Knowledge Economy, College of Management, National University of Political Studies and Public Administration, vol. 3(1), pages 133-149, March.
    46. 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.
    47. 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.
    48. Svetlana Makarova, 2018. "European Central Bank Footprints On Inflation Forecast Uncertainty," Economic Inquiry, Western Economic Association International, vol. 56(1), pages 637-652, January.
    49. Yoichi Tsuchiya, 2021. "Thirty‐year assessment of Asian Development Bank's forecasts," Asian-Pacific Economic Literature, The Crawford School, The Australian National University, vol. 35(2), pages 18-40, November.
    50. Todd E. Clark & Gergely Ganics & Elmar Mertens, 2022. "What is the Predictive Value of SPF Point and Density Forecasts?," Working Papers 22-37, Federal Reserve Bank of Cleveland.
    51. Tsuchiya, Yoichi, 2023. "Assessing the World Bank’s growth forecasts," Economic Analysis and Policy, Elsevier, vol. 77(C), pages 64-84.
    52. Lee, Seohyun, 2017. "Three essays on uncertainty: real and financial effects of uncertainty shocks," MPRA Paper 83617, University Library of Munich, Germany.
    53. MORIKAWA Masayuki, 2019. "Firms' Subjective Uncertainty and Forecast Errors," Discussion papers 19055, Research Institute of Economy, Trade and Industry (RIETI).
    54. Knüppel, Malte, 2014. "Forecast-error-based estimation of forecast uncertainty when the horizon is increased," Discussion Papers 40/2014, Deutsche Bundesbank.
    55. Michael P. Clements, 2020. "Do Survey Joiners and Leavers Differ from Regular Participants? The US SPF GDP Growth and Inflation Forecasts," ICMA Centre Discussion Papers in Finance icma-dp2020-01, Henley Business School, University of Reading.
    56. Charemza, Wojciech & Díaz, Carlos & Makarova, Svetlana, 2019. "Quasi ex-ante inflation forecast uncertainty," International Journal of Forecasting, Elsevier, vol. 35(3), pages 994-1007.
    57. Eyting, Markus & Schmidt, Patrick, 2021. "Belief elicitation with multiple point predictions," European Economic Review, Elsevier, vol. 135(C).
    58. Rülke, Jan-Christoph & Silgoner, Maria & Wörz, Julia, 2016. "Herding behavior of business cycle forecasters," International Journal of Forecasting, Elsevier, vol. 32(1), pages 23-33.
    59. Fabian Kruger & Hendrik Plett, 2022. "Prediction intervals for economic fixed-event forecasts," Papers 2210.13562, arXiv.org, revised Mar 2024.
    60. Yoichi Tsuchiya, 2021. "Crises, market shocks, and herding behavior in stock price forecasts," Empirical Economics, Springer, vol. 61(2), pages 919-945, August.
    61. Conrad, Christian & Glas, Alexander, 2018. "‘Déjà vol’ revisited: Survey forecasts of macroeconomic variables predict volatility in the cross-section of industry portfolios," Working Papers 0655, University of Heidelberg, Department of Economics.
    62. Yongchen Zhao, 2022. "Uncertainty and disagreement of inflation expectations: Evidence from household‐level qualitative survey responses," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(4), pages 810-828, July.
    63. Meyler, Aidan, 2020. "Forecast performance in the ECB SPF: ability or chance?," Working Paper Series 2371, European Central Bank.
    64. Tsuchiya, Yoichi, 2022. "Evaluating the European Central Bank’s uncertainty forecasts," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 321-330.

  27. Michael P. Clements, 2014. "US Inflation Expectations and Heterogeneous Loss Functions, 1968–2010," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(1), pages 1-14, January.
    See citations under working paper version above.
  28. Michael P. Clements & Ana Beatriz Galvão, 2013. "Real‐Time Forecasting Of Inflation And Output Growth With Autoregressive Models In The Presence Of Data Revisions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(3), pages 458-477, April.

    Cited by:

    1. Jeremy J. Nalewaik, 2014. "Missing Variation in the Great Moderation: Lack of Signal Error and OLS Regression," Finance and Economics Discussion Series 2014-27, Board of Governors of the Federal Reserve System (U.S.).
    2. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2015. "Robust approaches to forecasting," International Journal of Forecasting, Elsevier, vol. 31(1), pages 99-112.
    3. Jari Hännikäinen, 2014. "Multi-step forecasting in the presence of breaks," Working Papers 1494, Tampere University, Faculty of Management and Business, Economics.
    4. Hecq, Alain & Jacobs, Jan P.A.M. & Stamatogiannis, Michalis P., 2019. "Testing for news and noise in non-stationary time series subject to multiple historical revisions," Journal of Macroeconomics, Elsevier, vol. 60(C), pages 396-407.
    5. Clark, Todd & McCracken, Michael, 2013. "Advances in Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1107-1201, Elsevier.
    6. 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.
    7. Ana Beatriz Galvão & Marta Lopresto, 2020. "Real-time Probabilistic Nowcasts of UK Quarterly GDP Growth using a Mixed-Frequency Bottom-up Approach," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2020-06, Economic Statistics Centre of Excellence (ESCoE).
    8. Sinclair, Tara M., 2019. "Characteristics and implications of Chinese macroeconomic data revisions," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1108-1117.
    9. 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.
    10. Bec, Frédérique & Mogliani, Matteo, 2015. "Nowcasting French GDP in real-time with surveys and “blocked” regressions: Combining forecasts or pooling information?," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1021-1042.
    11. Kiss, Tamás & Mazur, Stepan & Nguyen, Hoang & Österholm, Pär, 2021. "Modelling the Relation between the US Real Economy and the Corporate Bond-Yield Spread in Bayesian VARs with non-Gaussian Disturbances," Working Papers 2021:9, Örebro University, School of Business.
    12. Afees A. Salisu & Raymond Swaray & Hadiza Sa'id, 2021. "Improving forecasting accuracy of the Phillips curve in OECD countries: The role of commodity prices," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2946-2975, April.
    13. 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.
    14. Andrew C. Chang & Tyler J. Hanson, 2015. "The Accuracy of Forecasts Prepared for the Federal Open Market Committee," Finance and Economics Discussion Series 2015-62, Board of Governors of the Federal Reserve System (U.S.).
    15. Chang, Andrew C. & Hanson, Tyler J., 2016. "The accuracy of forecasts prepared for the Federal Open Market Committee," Journal of Economics and Business, Elsevier, vol. 83(C), pages 23-43.
    16. Jari Hännikäinen, 2016. "Selection of an Estimation Window in the Presence of Data Revisions and Recent Structural Breaks," Working Papers 1692, Tampere University, Faculty of Management and Business, Economics.
    17. Kevin Lee & James Morley & Kalvinder Shields & Madeleine Sui-Lay Tan, 2018. "The Australian real-time fiscal database: An overview and an illustration of its use in analysing planned and realised fiscal policies," Discussion Papers 2018/11, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    18. Frédérique Bec & Patrick Kanda, 2019. "Is inflation driven by survey-based, VAR-based or myopic expectations?," Working Papers hal-02175836, HAL.
    19. M. Mogliani & T. Ferrière, 2016. "Rationality of announcements, business cycle asymmetry, and predictability of revisions. The case of French GDP," Working papers 600, Banque de France.
    20. 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.
    21. Galvao, Ana Beatriz, 2016. "Data Revisions and DSGE Models," EMF Research Papers 11, Economic Modelling and Forecasting Group.
    22. 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.
    23. 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.
    24. 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.
    25. Garcia, Márcio G.P. & Medeiros, Marcelo C. & Vasconcelos, Gabriel F.R., 2017. "Real-time inflation forecasting with high-dimensional models: The case of Brazil," International Journal of Forecasting, Elsevier, vol. 33(3), pages 679-693.
    26. 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.
    27. 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.
    28. 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.
    29. Strohsal, Till & Wolf, Elias, 2020. "Data revisions to German national accounts: Are initial releases good nowcasts?," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1252-1259.
    30. Michael P. Clements, 2017. "Assessing Macro Uncertainty in Real-Time When Data Are Subject To Revision," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 420-433, July.
    31. Bec, Frédérique & Kanda, Patrick, 2020. "Is inflation driven by survey-based, VAR-based or myopic expectations? An empirical assessment from US real-time data," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).

  29. Clements, Michael P. & Galvão, Ana Beatriz, 2013. "Forecasting with vector autoregressive models of data vintages: US output growth and inflation," International Journal of Forecasting, Elsevier, vol. 29(4), pages 698-714.

    Cited by:

    1. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2015. "Robust approaches to forecasting," International Journal of Forecasting, Elsevier, vol. 31(1), pages 99-112.
    2. 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.
    3. 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.
    4. Sinclair, Tara M., 2019. "Characteristics and implications of Chinese macroeconomic data revisions," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1108-1117.
    5. Magnus Kvåle Helliesen & Håvard Hungnes & Terje Skjerpen, 2022. "Revisions in the Norwegian National Accounts: accuracy, unbiasedness and efficiency in preliminary figures," Empirical Economics, Springer, vol. 62(3), pages 1079-1121, March.
    6. Michael P Clements & Ana Beatriz Galvao, 2017. "Data Revisions and Real-time Probabilistic Forecasting of Macroeconomic Variables," ICMA Centre Discussion Papers in Finance icma-dp2017-01, Henley Business School, University of Reading.
    7. 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).
    8. Galvao, Ana Beatriz & Mitchell, James & Runge, Johnny, 2019. "Communicating Data Uncertainty: Experimental Evidence for U.K. GDP," EMF Research Papers 30, Economic Modelling and Forecasting Group.
    9. Galvao, Ana Beatriz, 2016. "Data Revisions and DSGE Models," EMF Research Papers 11, Economic Modelling and Forecasting Group.
    10. 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. Denis Shibitov & Mariam Mamedli, 2021. "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series wps70, Bank of Russia.
    12. 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.
    13. 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.
    14. Michael P. Clements, 2017. "Assessing Macro Uncertainty in Real-Time When Data Are Subject To Revision," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 420-433, July.
    15. Michael P. Clements, 2014. "Anticipating Early Data Revisions to US GDP and the Effects of Releases on Equity Markets," ICMA Centre Discussion Papers in Finance icma-dp2014-06, Henley Business School, University of Reading.
    16. 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.
    17. Aparicio, Diego & Bertolotto, Manuel I., 2020. "Forecasting inflation with online prices," International Journal of Forecasting, Elsevier, vol. 36(2), pages 232-247.

  30. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2013. "Forecasting by factors, by variables, by both or neither?," Journal of Econometrics, Elsevier, vol. 177(2), pages 305-319.

    Cited by:

    1. Carlos, Thiago Carlomagno & Marçal, Emerson Fernandes, 2013. "Forecasting Brazilian inflation by its aggregate and disaggregated data: a test of predictive power by forecast horizon," Textos para discussão 346, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    2. 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.
    3. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2015. "Robust approaches to forecasting," International Journal of Forecasting, Elsevier, vol. 31(1), pages 99-112.
    4. Ericsson, Neil R., 2017. "Economic forecasting in theory and practice: An interview with David F. Hendry," International Journal of Forecasting, Elsevier, vol. 33(2), pages 523-542.
    5. Jack Fosten, 2016. "Model selection with factors and variables," University of East Anglia School of Economics Working Paper Series 2016-07, School of Economics, University of East Anglia, Norwich, UK..
    6. Niu, Linlin & Xu, Xiu & Chen, Ying, 2015. "An adaptive approach to forecasting three key macroeconomic variables for transitional China," BOFIT Discussion Papers 12/2015, Bank of Finland Institute for Emerging Economies (BOFIT).
    7. Charles Rahal, 2015. "Housing Market Forecasting with Factor Combinations," Discussion Papers 15-05, Department of Economics, University of Birmingham.
    8. Pinto, Jeronymo Marcondes & Marçal, Emerson Fernandes, 2019. "Cross-validation based forecasting method: a machine learning approach," Textos para discussão 498, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    9. Neil R. Ericsson, 2017. "How Biased Are U.S. Government Forecasts of the Federal Debt?," International Finance Discussion Papers 1189, Board of Governors of the Federal Reserve System (U.S.).
    10. Emmanuel Flachaire & Gilles Hacheme & Sullivan Hu'e & S'ebastien Laurent, 2022. "GAM(L)A: An econometric model for interpretable Machine Learning," Papers 2203.11691, arXiv.org.
    11. 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.
    12. Neil R. Ericsson, 2021. "Dynamic Econometrics in Action: A Biography of David F. Hendry," International Finance Discussion Papers 1311, Board of Governors of the Federal Reserve System (U.S.).
    13. Anh Dinh Minh Nguyen, 2017. "U.K. Monetary Policy under Inflation Targeting," Bank of Lithuania Working Paper Series 41, Bank of Lithuania.
    14. 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.
    15. Dellas, Harris & Gibson, Heather D. & Hall, Stephen G. & Tavlas, George S., 2018. "The macroeconomic and fiscal implications of inflation forecast errors," Journal of Economic Dynamics and Control, Elsevier, vol. 93(C), pages 203-217.
    16. Clements, Michael P., 2016. "Real-time factor model forecasting and the effects of instability," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 661-675.
    17. Sullivan Hué, 2022. "GAM(L)A: An econometric model for interpretable machine learning," French Stata Users' Group Meetings 2022 19, Stata Users Group.
    18. 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.
    19. Jack Fosten, 2016. "Forecast evaluation with factor-augmented models," University of East Anglia School of Economics Working Paper Series 2016-05, School of Economics, University of East Anglia, Norwich, UK..
    20. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    21. Neil R. Ericsson & Mohammed H. I. Dore & Hassan Butt, 2022. "Detecting and Quantifying Structural Breaks in Climate," Econometrics, MDPI, vol. 10(4), pages 1-27, November.
    22. Michael S. Lee-Browne, 2019. "Estimating monetary policy rules in small open economies," Working Papers 2019-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    23. Yuxuan Huang, 2016. "Forecasting the USD/CNY Exchange Rate under Different Policy Regimes," Working Papers 2016-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    24. Valentina Corradi & Norman Swanson, 2013. "Testing for Structural Stability of Factor Augmented Forecasting Models," Departmental Working Papers 201314, Rutgers University, Department of Economics.

  31. Clements Michael P., 2012. "Forecasting U.S. Output Growth with Non-Linear Models in the Presence of Data Uncertainty," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(1), pages 1-27, January.

    Cited by:

    1. 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.
    2. 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. Michael P. Clements & Ana Beatriz Galvão, 2012. "Improving Real-Time Estimates of Output and Inflation Gaps With Multiple-Vintage Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 554-562, May.

    Cited by:

    1. Adam J. Check & Anna K Nolan & Tyler C. Schipper, 2019. "Forecasting GDP Growth using Disaggregated GDP Revisions," Economics Bulletin, AccessEcon, vol. 39(4), pages 2580-2588.
    2. Chalmovianský, Jakub & Němec, Daniel, 2022. "Assessing uncertainty of output gap estimates: Evidence from Visegrad countries," Economic Modelling, Elsevier, vol. 116(C).
    3. 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.
    4. Ana Beatriz Galvão & James Mitchell, 2019. "Measuring Data Uncertainty: An Application using the Bank of England's "Fan Charts" for Historical GDP Growth," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2019-08, Economic Statistics Centre of Excellence (ESCoE).
    5. 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.
    6. Ana Beatriz Galvão & James Mitchell, 2023. "Real‐Time Perceptions of Historical GDP Data Uncertainty," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 457-481, June.
    7. Michael P. Clements, 2014. "Long-Run Restrictions and Survey Forecasts of Output, Consumption and Investment," ICMA Centre Discussion Papers in Finance icma-dp2014-02, Henley Business School, University of Reading.
    8. Clements, Michael P., 2016. "Real-time factor model forecasting and the effects of instability," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 661-675.
    9. 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.
    10. Michael P. Clements, 2017. "Assessing Macro Uncertainty in Real-Time When Data Are Subject To Revision," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 420-433, July.
    11. Michael P. Clements, 2014. "Anticipating Early Data Revisions to US GDP and the Effects of Releases on Equity Markets," ICMA Centre Discussion Papers in Finance icma-dp2014-06, Henley Business School, University of Reading.

  33. Clements, Michael P., 2012. "Do professional forecasters pay attention to data releases?," International Journal of Forecasting, Elsevier, vol. 28(2), pages 297-308.
    See citations under working paper version above.
  34. Clements, Michael P. & Harvey, David I., 2011. "Combining probability forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 208-223, April.

    Cited by:

    1. Shaun P Vahey & Elizabeth C Wakerly, 2013. "Moving towards probability forecasting," BIS Papers chapters, in: Bank for International Settlements (ed.), Globalisation and inflation dynamics in Asia and the Pacific, volume 70, pages 3-8, Bank for International Settlements.
    2. Pauwels, Laurent & Vasnev, Andrey, 2013. "Forecast combination for U.S. recessions with real-time data," Working Papers 2013-05, University of Sydney Business School, Discipline of Business Analytics.
    3. Ruben Loaiza-Maya & Gael M. Martin & David T. Frazier & Worapree Maneesoonthorn & Andres Ramirez Hassan, 2020. "Optimal probabilistic forecasts: When do they work?," Monash Econometrics and Business Statistics Working Papers 33/20, Monash University, Department of Econometrics and Business Statistics.
    4. Lyon, Aidan & Wintle, Bonnie C. & Burgman, Mark, 2015. "Collective wisdom: Methods of confidence interval aggregation," Journal of Business Research, Elsevier, vol. 68(8), pages 1759-1767.
    5. Bentes, Sonia R. & Menezes, Rui, 2013. "On the predictability of realized volatility using feasible GLS," Journal of Asian Economics, Elsevier, vol. 28(C), pages 58-66.
    6. Monica Billio & Roberto Casarin & Francesco Ravazzolo & Herman K. van Dijk, 2012. "Combination schemes for turning point predictions," Working Paper 2012/04, Norges Bank.
    7. Qian, Wei & Rolling, Craig A. & Cheng, Gang & Yang, Yuhong, 2022. "Combining forecasts for universally optimal performance," International Journal of Forecasting, Elsevier, vol. 38(1), pages 193-208.
    8. Ruben Loaiza‐Maya & Gael M. Martin & David T. Frazier, 2021. "Focused Bayesian prediction," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 517-543, August.
    9. Pablo Pincheira, 2012. "Are Forecast Combinations Efficient?," Working Papers Central Bank of Chile 661, Central Bank of Chile.
    10. Cristina Conflitti & Christine De Mol & Domenico Giannone, 2012. "Optimal Combination of Survey Forecasts," Working Papers ECARES ECARES 2012-023, ULB -- Universite Libre de Bruxelles.
    11. Stephen Hora & Erim Kardeş, 2015. "Calibration, sharpness and the weighting of experts in a linear opinion pool," Annals of Operations Research, Springer, vol. 229(1), pages 429-450, June.
    12. Fabian Krüger & Ingmar Nolte, 2011. "Disagreement, Uncertainty and the True Predictive Density," Working Paper Series of the Department of Economics, University of Konstanz 2011-43, Department of Economics, University of Konstanz.
    13. Pirschel, Inske, 2016. "Forecasting euro area recessions in real-time," Kiel Working Papers 2020, Kiel Institute for the World Economy (IfW Kiel).
    14. Rodrigues, Bruno Dore & Stevenson, Maxwell J., 2013. "Takeover prediction using forecast combinations," International Journal of Forecasting, Elsevier, vol. 29(4), pages 628-641.
    15. Kajal Lahiri & Huaming Peng & Yongchen Zhao, 2013. "Testing the Value of Probability Forecasts for Calibrated Combining," Discussion Papers 13-02, University at Albany, SUNY, Department of Economics.
    16. Frederik Kunze, 2020. "Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(2), pages 313-333, March.
    17. 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).
    18. Laurent L. Pauwels & Andrey L. Vasnev, 2017. "Forecast combination for discrete choice models: predicting FOMC monetary policy decisions," Empirical Economics, Springer, vol. 52(1), pages 229-254, February.
    19. 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.
    20. Pablo Pincheira-Brown & Andrea Bentancor & Nicolás Hardy, 2023. "An Inconvenient Truth about Forecast Combinations," Mathematics, MDPI, vol. 11(18), pages 1-24, September.
    21. 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.
    22. Bermejo Mancera, Miguel Ángel & Peña, Daniel & Sánchez, Ismael, 2011. "Densidad de predicción basada en momentos condicionados y máxima entropía : aplicación a la predicción de potencia eólica," DES - Working Papers. Statistics and Econometrics. WS ws111813, Universidad Carlos III de Madrid. Departamento de Estadística.
    23. Trapero, Juan R. & Cardós, Manuel & Kourentzes, Nikolaos, 2019. "Quantile forecast optimal combination to enhance safety stock estimation," International Journal of Forecasting, Elsevier, vol. 35(1), pages 239-250.
    24. Lee, Seohyun, 2017. "Three essays on uncertainty: real and financial effects of uncertainty shocks," MPRA Paper 83617, University Library of Munich, Germany.
    25. Michael P. Clements, 2020. "Are Some Forecasters’ Probability Assessments of Macro Variables Better Than Those of Others?," Econometrics, MDPI, vol. 8(2), pages 1-16, May.
    26. Giovanni De Luca & Alfonso Carfora, 2014. "Predicting U.S. recessions through a combination of probability forecasts," Empirical Economics, Springer, vol. 46(1), pages 127-144, February.

  35. Michael P. Clements, 2011. "An Empirical Investigation of the Effects of Rounding on the SPF Probabilities of Decline and Output Growth Histograms," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(1), pages 207-220, February.

    Cited by:

    1. Glas, Alexander, 2020. "Five dimensions of the uncertainty–disagreement linkage," International Journal of Forecasting, Elsevier, vol. 36(2), pages 607-627.
    2. Alexander Glas & Matthias Hartmann, 2022. "Uncertainty measures from partially rounded probabilistic forecast surveys," Quantitative Economics, Econometric Society, vol. 13(3), pages 979-1022, July.
    3. Zhao, Yongchen, 2023. "Internal consistency of household inflation expectations: Point forecasts vs. density forecasts," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1713-1735.
    4. Kajal Lahiri & Huaming Peng & Yongchen Zhao, 2013. "Testing the Value of Probability Forecasts for Calibrated Combining," Discussion Papers 13-02, University at Albany, SUNY, Department of Economics.
    5. Lahiri, Kajal & Wang, J. George, 2013. "Evaluating probability forecasts for GDP declines using alternative methodologies," International Journal of Forecasting, Elsevier, vol. 29(1), pages 175-190.
    6. Clements, Michael P., 2021. "Rounding behaviour of professional macro-forecasters," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1614-1631.
    7. 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.
    8. 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.

  36. Clements, Michael P. & Beatriz Galvão, Ana, 2010. "First announcements and real economic activity," European Economic Review, Elsevier, vol. 54(6), pages 803-817, August.
    See citations under working paper version above.
  37. Clements, Michael P., 2010. "Explanations of the inconsistencies in survey respondents' forecasts," European Economic Review, Elsevier, vol. 54(4), pages 536-549, May.
    See citations under working paper version above.
  38. Michael P. Clements & David I. Harvey, 2010. "Forecast encompassing tests and probability forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(6), pages 1028-1062.
    See citations under working paper version above.
  39. Clements, Michael P., 2009. "Comments on "Forecasting economic and financial variables with global VARs"," International Journal of Forecasting, Elsevier, vol. 25(4), pages 680-683, October.

    Cited by:

    1. Helmut Herwartz, 2011. "Forecast accuracy and uncertainty in applied econometrics: a recommendation of specific-to-general predictor selection," Empirical Economics, Springer, vol. 41(2), pages 487-510, October.

  40. Michael P. Clements & Ana Beatriz Galvao, 2009. "Forecasting US output growth using leading indicators: an appraisal using MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1187-1206.

    Cited by:

    1. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Economics Working Papers ECO2013/02, European University Institute.
    2. 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.
    3. 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.
    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.
    5. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    6. 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.
    7. Qian, Hang, 2012. "Essays on statistical inference with imperfectly observed data," ISU General Staff Papers 201201010800003618, Iowa State University, Department of Economics.
    8. 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.
    9. Afees A. Salisu & Ahamuefula Ephraim Ogbonna, 2017. "Improving the Predictive ability of oil for inflation: An ADL-MIDAS Approach," Working Papers 025, Centre for Econometric and Allied Research, University of Ibadan.
    10. Christiane Baumeister & Pierre Guérin, 2020. "A Comparison of Monthly Global Indicators for Forecasting Growth," CESifo Working Paper Series 8656, CESifo.
    11. 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.
    12. 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.
    13. Winkelried, Diego, 2012. "Predicting quarterly aggregates with monthly indicators," Working Papers 2012-023, Banco Central de Reserva del Perú.
    14. 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.
    15. Marcellino, Massimiliano, 2011. "Markov-switching MIDAS models," CEPR Discussion Papers 8234, C.E.P.R. Discussion Papers.
    16. 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.
    17. Michal Franta & David Havrlant & Marek Rusnák, 2016. "Forecasting Czech GDP Using Mixed-Frequency Data Models," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(2), pages 165-185, December.
    18. 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.
    19. 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.
    20. Stefan Neuwirth, 2017. "Time-varying mixed frequency forecasting: A real-time experiment," KOF Working papers 17-430, KOF Swiss Economic Institute, ETH Zurich.
    21. João Valle e Azevedo & Inês Maria Gonçalves, 2015. "Macroeconomic Forecasting Starting from Survey Nowcasts," Working Papers w201502, Banco de Portugal, Economics and Research Department.
    22. 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.
    23. Afees A. Salisu & Umar B. Ndako & Idris Adediran, 2018. "Forecasting GDP of OPEC: The role of oil price," Working Papers 044, Centre for Econometric and Allied Research, University of Ibadan.
    24. Katja Drechsel & Dr. Rolf Scheufele, 2012. "Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment," Working Papers 2012-16, Swiss National Bank.
    25. Götz, T.B. & Hecq, A.W., 2014. "Testing for Granger causality in large mixed-frequency VARs," Research Memorandum 028, Maastricht University, Graduate School of Business and Economics (GSBE).
    26. 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.
    27. Laurent Ferrara & Pierre Guérin, 2015. "What Are The Macroeconomic Effects of High-Frequency Uncertainty Shocks?," EconomiX Working Papers 2015-12, University of Paris Nanterre, EconomiX.
    28. Robert Lehmann & Klaus Wohlrabe, 2012. "Forecasting GDP at the Regional Level with Many Predictors," CESifo Working Paper Series 3956, CESifo.
    29. 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.
    30. Danilo Cascaldi-Garcia & Matteo Luciani & Michele Modugno, 2023. "Lessons from Nowcasting GDP across the World," International Finance Discussion Papers 1385, Board of Governors of the Federal Reserve System (U.S.).
    31. Ferrara, Laurent & Mogliani, Matteo & Sahuc, Jean-Guillaume, 2022. "High-frequency monitoring of growth at risk," International Journal of Forecasting, Elsevier, vol. 38(2), pages 582-595.
    32. Boriss Siliverstovs, 2015. "Short-term forecasting with mixed-frequency data: A MIDASSO approach," KOF Working papers 15-375, KOF Swiss Economic Institute, ETH Zurich.
    33. 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.
    34. Bec, Frédérique & Mogliani, Matteo, 2015. "Nowcasting French GDP in real-time with surveys and “blocked” regressions: Combining forecasts or pooling information?," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1021-1042.
    35. 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.
    36. 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.
    37. Turgut Kisinbay & Chikako Baba, 2011. "Predicting Recessions: A New Approach for Identifying Leading Indicators and Forecast Combinations," IMF Working Papers 2011/235, International Monetary Fund.
    38. 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).
    39. Jack Fosten & Shaoni Nandi, 2023. "Nowcasting from cross‐sectionally dependent panels," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(6), pages 898-919, September.
    40. 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.
    41. 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.
    42. 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.
    43. Dr. Sandra Hanslin Grossmann & Dr. Rolf Scheufele, 2016. "Foreign PMIs: A reliable indicator for exports?," Working Papers 2016-01, Swiss National Bank.
    44. 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.
    45. Katja Heinisch & Rolf Scheufele, 2019. "Should Forecasters Use Real‐Time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence," German Economic Review, Verein für Socialpolitik, vol. 20(4), pages 170-200, November.
    46. Matteo Mogliani, 2019. "Bayesian MIDAS penalized regressions: estimation, selection, and prediction," Working papers 713, Banque de France.
    47. 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.
    48. Drechsel, Katja & Giesen, Sebastian & Lindner, Axel, 2014. "Outperforming IMF Forecasts by the Use of Leading Indicators," IWH Discussion Papers 4/2014, Halle Institute for Economic Research (IWH).
    49. 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.
    50. Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013. "Now-Casting and the Real-Time Data Flow," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237, Elsevier.
    51. Nava, Consuelo R. & Osti, Linda & Zoia, Maria Grazia, 2022. "Forecasting Domestic Tourism across Regional Destinations through MIDAS Regressions," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202207, University of Turin.
    52. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil prices," MPRA Paper 77531, University Library of Munich, Germany.
    53. Clements, Michael P. & Beatriz Galvao, Ana, 2008. "First Announcements and Real Economic Activity," Economic Research Papers 271314, University of Warwick - Department of Economics.
    54. Warmedinger, Thomas & Paredes, Joan & Asimakopoulos, Stylianos, 2013. "Forecasting fiscal time series using mixed frequency data," Working Paper Series 1550, European Central Bank.
    55. Andrejs Bessonovs, 2015. "Suite of Latvia's GDP forecasting models," Working Papers 2015/01, Latvijas Banka.
    56. 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.
    57. 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.
    58. Peter Fuleky & Carl S. Bonham, 2011. "Forecasting Based on Common Trends in Mixed Frequency Samples," Working Papers 201110, University of Hawaii at Manoa, Department of Economics.
    59. 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.
    60. Clements, Michael P. & Galvão, Ana Beatriz, 2010. "Real-time Forecasting of Inflation and Output Growth in the Presence of Data Revisions," The Warwick Economics Research Paper Series (TWERPS) 953, University of Warwick, Department of Economics.
    61. 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.
    62. 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.
    63. 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.
    64. 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.
    65. 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.
    66. Francesco Ravazzolo & Joaquin Vespignani, 2017. "World steel production: A new monthly indicator of global real economic activity," CAMA Working Papers 2017-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    67. Clements Michael P., 2012. "Forecasting U.S. Output Growth with Non-Linear Models in the Presence of Data Uncertainty," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(1), pages 1-27, January.
    68. Thomas B. Götz & Alain Hecq & Jean‐Pierre Urbain, 2014. "Forecasting Mixed‐Frequency Time Series with ECM‐MIDAS Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(3), pages 198-213, April.
    69. 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.
    70. 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.
    71. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
    72. Kiygi-Calli, Meltem & Weverbergh, Marcel & Franses, Philip Hans, 2017. "Modeling intra-seasonal heterogeneity in hourly advertising-response models: Do forecasts improve?," International Journal of Forecasting, Elsevier, vol. 33(1), pages 90-101.
    73. 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.
    74. 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.
    75. Peter Fuleky & Carl S. Bonham, 2013. "Forecasting with Mixed Frequency Samples: The Case of Common Trends," Working Papers 201316, University of Hawaii at Manoa, Department of Economics.
    76. 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.
    77. 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.
    78. 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.
    79. 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.
    80. Lynda Khalaf & Maral Kichian & Charles Saunders & Marcel Voia, 2021. "Dynamic panels with MIDAS covariates: Nonlinearity, estimation and fit," Post-Print hal-03528880, HAL.
    81. 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.
    82. Wang, Ruina & Li, Jinfang, 2021. "The influence and predictive powers of mixed-frequency individual stock sentiment on stock returns," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    83. 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).
    84. Bańbura, Marta & Belousova, Irina & Bodnár, Katalin & Tóth, Máté Barnabás, 2023. "Nowcasting employment in the euro area," Working Paper Series 2815, European Central Bank.
    85. Miller, J. Isaac, 2018. "Simple robust tests for the specification of high-frequency predictors of a low-frequency series," Econometrics and Statistics, Elsevier, vol. 5(C), pages 45-66.
    86. 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.
    87. 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.
    88. Alejandro Fernández Cerezo, 2023. "A supply-side GDP nowcasting model," Economic Bulletin, Banco de España, issue 2023/Q1.
    89. Denis Shibitov & Mariam Mamedli, 2021. "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series wps70, Bank of Russia.
    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. 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.
    92. 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.
    93. He, Yongda & Lin, Boqiang, 2018. "Forecasting China's total energy demand and its structure using ADL-MIDAS model," Energy, Elsevier, vol. 151(C), pages 420-429.
    94. 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.
    95. 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.
    96. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2014. "Density forecasts with MIDAS models," Working Paper 2014/10, Norges Bank.
    97. 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).
    98. an de Meulen, Philipp, 2015. "Das RWI-Kurzfristprognosemodell," RWI Konjunkturberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, vol. 66(2), pages 25-46.
    99. Adam Nowak & Patrick Smith, 2015. "Textual Analysis in Real Estate," Working Papers 15-34, Department of Economics, West Virginia University.
    100. Stavros Degiannakis, 2023. "The D-model for GDP nowcasting," Working Papers 317, Bank of Greece.
    101. Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.
    102. 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.
    103. 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.
    104. João Valle e Azevedo, 2013. "Macroeconomic Forecasting Using Low-Frequency Filters," Working Papers w201301, Banco de Portugal, Economics and Research Department.
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    Cited by:

    1. Claudia Foroni & Massimiliano Marcellino, 2013. "A survey of econometric methods for mixed-frequency data," Economics Working Papers ECO2013/02, European University Institute.
    2. Salisu, Afees A. & Gupta, Rangan & Demirer, Riza, 2022. "Global financial cycle and the predictability of oil market volatility: Evidence from a GARCH-MIDAS model," Energy Economics, Elsevier, vol. 108(C).
    3. Rodriguez, Abel & Puggioni, Gavino, 2010. "Mixed frequency models: Bayesian approaches to estimation and prediction," International Journal of Forecasting, Elsevier, vol. 26(2), pages 293-311, April.
    4. Afees A. Salisu & Ahamuefula E. Ogbonna & Rangan Gupta & Qiang Ji, 2024. "Energy Market Uncertainties and Exchange Rate Volatility: A GARCH-MIDAS Approach," Working Papers 202418, University of Pretoria, Department of Economics.
    5. 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).
    6. 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.
    7. Galvão, Ana Beatriz, 2013. "Changes in predictive ability with mixed frequency data," International Journal of Forecasting, Elsevier, vol. 29(3), pages 395-410.
    8. 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.
    9. 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.
    10. Xu, Qifa & Xu, Mengnan & Jiang, Cuixia & Fu, Weizhong, 2023. "Mixed-frequency Growth-at-Risk with the MIDAS-QR method: Evidence from China," Economic Systems, Elsevier, vol. 47(4).
    11. 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.
    12. 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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    112. 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, March.
    113. 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.
    114. 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.
    115. Jansen, W. Jos & Jin, Xiaowen & de Winter, Jasper M., 2016. "Forecasting and nowcasting real GDP: Comparing statistical models and subjective forecasts," International Journal of Forecasting, Elsevier, vol. 32(2), pages 411-436.
    116. Lynda Khalaf & Maral Kichian & Charles Saunders & Marcel Voia, 2021. "Dynamic panels with MIDAS covariates: Nonlinearity, estimation and fit," Post-Print hal-03528880, HAL.
    117. 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).
    118. Philip Hans Franses & Eva Janssens, 2017. "Recovering Historical Inflation Data from Postage Stamps Prices," JRFM, MDPI, vol. 10(4), pages 1-11, November.
    119. 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.
    120. Ulrich Gunter & Irem Önder & Stefan Gindl, 2019. "Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria," Tourism Economics, , vol. 25(3), pages 375-401, May.
    121. Miller, J. Isaac, 2018. "Simple robust tests for the specification of high-frequency predictors of a low-frequency series," Econometrics and Statistics, Elsevier, vol. 5(C), pages 45-66.
    122. 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.
    123. 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.
    124. Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2020. "The Role of Global Economic Conditions in Forecasting Gold Market Volatility: Evidence from a GARCH-MIDAS Approach," Working Papers 202043, University of Pretoria, Department of Economics.
    125. 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.
    126. Alejandro Fernández Cerezo, 2023. "A supply-side GDP nowcasting model," Economic Bulletin, Banco de España, issue 2023/Q1.
    127. 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.
    128. Afees A. Salisu & Rangan Gupta & Riza Demirer, 2020. "A Note on Uncertainty due to Infectious Diseases and Output Growth of the United States: A Mixed-Frequency Forecasting Experiment," Working Papers 202050, University of Pretoria, Department of Economics.
    129. Tony Chernis & Calista Cheung & Gabriella Velasco, 2017. "A Three-Frequency Dynamic Factor Model for Nowcasting Canadian Provincial GDP Growth," Discussion Papers 17-8, Bank of Canada.
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    133. 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.
    134. 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.
    135. He, Yongda & Lin, Boqiang, 2018. "Forecasting China's total energy demand and its structure using ADL-MIDAS model," Energy, Elsevier, vol. 151(C), pages 420-429.
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    138. Afees A. Salisu & Ahamuefula E. Ogbonna & Rangan Gupta & Elie Bouri, 2023. "Energy-Related Uncertainty and International Stock Market Volatility," Working Papers 202336, University of Pretoria, Department of Economics.
    139. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2014. "Density forecasts with MIDAS models," Working Paper 2014/10, Norges Bank.
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    141. Jianhao Lin & Jiacheng Fan & Yifan Zhang & Liangyuan Chen, 2023. "Real‐time macroeconomic projection using narrative central bank communication," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 202-221, March.
    142. Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.
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    144. Angelos Kanas & Panagiotis D. Zervopoulos, 2020. "Systemic risk-shifting in U.S. commercial banking," Review of Quantitative Finance and Accounting, Springer, vol. 54(2), pages 517-539, February.
    145. Dorin JULA & Nicolae Marius JULA, 2017. "Mixed Sampling Panel Data Model for Regional Job Vacancies Forecasting," Eco-Economics Review, Ecological University of Bucharest, Economics Faculty and Ecology and Environmental Protection Faculty, vol. 3(1), pages 3-20, June.
    146. 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.
    147. 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.
    148. 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.
    149. 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.
    150. João Valle e Azevedo, 2013. "Macroeconomic Forecasting Using Low-Frequency Filters," Working Papers w201301, Banco de Portugal, Economics and Research Department.
    151. 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).
    152. Yunxu Wang & Chi-Wei Su & Yuchen Zhang & Oana-Ramona Lobonţ & Qin Meng, 2023. "Effectiveness of Principal-Component-Based Mixed-Frequency Error Correction Model in Predicting Gross Domestic Product," Mathematics, MDPI, vol. 11(19), pages 1-14, September.
    153. Thomas Walther & Lanouar Charfeddine & Tony Klein, 2018. "Oil Price Changes and U.S. Real GDP Growth: Is this Time Different?," Working Papers on Finance 1816, University of St. Gallen, School of Finance.
    154. Loechel, Horst & Packham, Natalie & Walisch, Fabian, 2013. "Determinants of the onshore and offshore Chinese Government yield curves," Frankfurt School - Working Paper Series 202, Frankfurt School of Finance and Management.
    155. Kihwan Kim & Hyun Hak Kim & Norman R. Swanson, 2023. "Mixing mixed frequency and diffusion indices in good times and in bad: an assessment based on historical data around the great recession of 2008," Empirical Economics, Springer, vol. 64(3), pages 1421-1469, March.
    156. Clements, Michael P. & Reade, J. James, 2020. "Forecasting and forecast narratives: The Bank of England Inflation Reports," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1488-1500.
    157. 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.
    158. 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.
    159. Foroni, Claudia & Ravazzolo, Francesco & Rossini, Luca, 2019. "Forecasting daily electricity prices with monthly macroeconomic variables," Working Paper Series 2250, European Central Bank.
    160. Degiannakis, Stavros & Filis, George, 2018. "Forecasting oil prices: High-frequency financial data are indeed useful," Energy Economics, Elsevier, vol. 76(C), pages 388-402.
    161. 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.
    162. Mont'Alverne Duarte, Angelo & Gaglianone, Wagner Piazza & de Carvalho Guillén, Osmani Teixeira & Issler, João Victor, 2021. "Commodity prices and global economic activity: A derived-demand approach," Energy Economics, Elsevier, vol. 96(C).
    163. 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.
    164. 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.
    165. Barhoumi, K. & Darné, O. & Ferrara, L., 2013. "Dynamic Factor Models: A review of the Literature ," Working papers 430, Banque de France.
    166. 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.
    167. Qian, Hang, 2010. "Vector autoregression with varied frequency data," MPRA Paper 34682, University Library of Munich, Germany.
    168. Ojogho, Osaihiomwan & Egware, Robert Awotu, 2015. "Price Generating Process And Volatility In Nigerian Agricultural Commodities Market," International Journal of Food and Agricultural Economics (IJFAEC), Alanya Alaaddin Keykubat University, Department of Economics and Finance, vol. 3(4), pages 1-10, October.
    169. 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.
    170. 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.
    171. Schumacher, Christian, 2016. "A comparison of MIDAS and bridge equations," International Journal of Forecasting, Elsevier, vol. 32(2), pages 257-270.
    172. 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.
    173. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2013. "Forecasting by factors, by variables, by both or neither?," Journal of Econometrics, Elsevier, vol. 177(2), pages 305-319.
    174. 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.
    175. 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).
    176. Götz, Thomas B. & Hauzenberger, Klemens, 2018. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure," Discussion Papers 40/2018, Deutsche Bundesbank.
    177. Huiwen Lai & Eric C. Y. Ng, 2020. "On business cycle forecasting," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-26, December.
    178. Guay, Alain & Maurin, Alain, 2015. "Disaggregation methods based on MIDAS regression," Economic Modelling, Elsevier, vol. 50(C), pages 123-129.
    179. Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2020. "Forecasting Oil Volatility Using a GARCH-MIDAS Approach: The Role of Global Economic Conditions," Working Papers 202051, University of Pretoria, Department of Economics.
    180. Marcellino, Massimiliano & Foroni, Claudia, 2014. "Markov-Switching Mixed-Frequency VAR Models," CEPR Discussion Papers 9815, C.E.P.R. Discussion Papers.
    181. 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.
    182. Serena Ng & Susannah Scanlan, 2023. "Constructing High Frequency Economic Indicators by Imputation," Papers 2303.01863, arXiv.org, revised Oct 2023.
    183. 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, March.
    184. 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.
    185. 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).
    186. 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.
    187. Afees A. Salisu & Ahamuefula E.Oghonna & Rangan Gupta & Oguzhan Cepni, 2024. "Energy Market Uncertainties and US State-Level Stock Market Volatility: A GARCH-MIDAS Approach," Working Papers 202409, University of Pretoria, Department of Economics.
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    189. Teresa Leal & Diego Pedregal & Javier Pérez, 2011. "Short-term monitoring of the Spanish government balance," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 2(1), pages 97-119, March.
    190. 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.
    191. 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.
    192. 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.

  42. Clements, Michael P. & Galvão, Ana Beatriz & Kim, Jae H., 2008. "Quantile forecasts of daily exchange rate returns from forecasts of realized volatility," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 729-750, September.
    See citations under working paper version above.
  43. Clements, Michael P., 2008. "Consensus and uncertainty: Using forecast probabilities of output declines," International Journal of Forecasting, Elsevier, vol. 24(1), pages 76-86.

    Cited by:

    1. Clements, Michael P., 2008. "Explanations of the inconsistencies in survey respondents'forecasts," The Warwick Economics Research Paper Series (TWERPS) 870, University of Warwick, Department of Economics.
    2. Hristov, Nikolay & Roth, Markus, 2019. "Uncertainty shocks and financial crisis indicators," Discussion Papers 36/2019, Deutsche Bundesbank.
    3. Masayuki Morikawa, 2016. "What Types of Policy Uncertainties Matter for Business?," Pacific Economic Review, Wiley Blackwell, vol. 21(5), pages 527-540, December.
    4. Yoshiyuki Arata & Yosuke Kimura & Hiroki Murakami, 2017. "Aggregate implications of lumpy investment under heterogeneity and uncertainty: a model of collective behavior," Evolutionary and Institutional Economics Review, Springer, vol. 14(2), pages 311-333, December.
    5. ARATA Yoshiyuki & KIMURA Yosuke & MURAKAMI Hiroki, 2015. "Macroeconomic Consequences of Lumpy Investment under Uncertainty," Discussion papers 15120, Research Institute of Economy, Trade and Industry (RIETI).
    6. Clements, Michael P., 2008. "Rounding of probability forecasts: The SPF forecast probabilities of negative output growth," Economic Research Papers 269880, University of Warwick - Department of Economics.
    7. Kajal Lahiri & Huaming Peng & Yongchen Zhao, 2013. "Testing the Value of Probability Forecasts for Calibrated Combining," Discussion Papers 13-02, University at Albany, SUNY, Department of Economics.
    8. 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.
    9. ChiUng Song & Bryan L. Boulier & Herman O. Stekler, 2008. "Measuring Consensus in Binary Forecasts: NFL Game Predictions," Working Papers 2008-006, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    10. 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).
    11. Sharpe, Steven A. & Sinha, Nitish R. & Hollrah, Christopher A., 2023. "The power of narrative sentiment in economic forecasts," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1097-1121.
    12. Lahiri, Kajal & Wang, J. George, 2013. "Evaluating probability forecasts for GDP declines using alternative methodologies," International Journal of Forecasting, Elsevier, vol. 29(1), pages 175-190.
    13. Cho, Dooyeon & Kim, Husang, 2023. "Macroeconomic effects of uncertainty shocks: Evidence from Korea," Journal of Asian Economics, Elsevier, vol. 84(C).
    14. Hristov, Nikolay & Roth, Markus, 2022. "Uncertainty shocks and systemic-risk indicators," Journal of International Money and Finance, Elsevier, vol. 122(C).
    15. Kajal Lahiri & Yongchen Zhao, 2016. "Determinants of Consumer Sentiment over Business Cycles: Evidence from the U.S. Surveys of Consumers," Working Papers 2016-14, Towson University, Department of Economics, revised Jul 2016.
    16. MORIKAWA Masayuki, 2016. "Business Uncertainty and Investment: Evidence from Japanese companies," Discussion papers 16014, Research Institute of Economy, Trade and Industry (RIETI).
    17. Sergey V. Smirnov & Daria A. Avdeeva, 2016. "Wishful Bias in Predicting Us Recessions: Indirect Evidence," HSE Working papers WP BRP 135/EC/2016, National Research University Higher School of Economics.
    18. Michael P. Clements, 2011. "An Empirical Investigation of the Effects of Rounding on the SPF Probabilities of Decline and Output Growth Histograms," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(1), pages 207-220, February.
    19. MORIKAWA Masayuki, 2019. "Firms' Subjective Uncertainty and Forecast Errors," Discussion papers 19055, Research Institute of Economy, Trade and Industry (RIETI).
    20. 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.

  44. Clements Michael P. & Hendry David F., 2008. "Economic Forecasting in a Changing World," Capitalism and Society, De Gruyter, vol. 3(2), pages 1-20, October.

    Cited by:

    1. Gunnar Bårdsen & Dag Kolsrud & Ragnar Nymoen, 2017. "Forecast robustness in macroeconometric models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(6), pages 629-639, September.
    2. Fakhri J. Hasanov & Lester C. Hunt & Ceyhun I. Mikayilov, 2016. "Modeling and Forecasting Electricity Demand in Azerbaijan Using Cointegration Techniques," Energies, MDPI, vol. 9(12), pages 1-31, December.
    3. Niu, Linlin & Xu, Xiu & Chen, Ying, 2015. "An adaptive approach to forecasting three key macroeconomic variables for transitional China," BOFIT Discussion Papers 12/2015, Bank of Finland Institute for Emerging Economies (BOFIT).
    4. David Mortimer Krainz, 2011. "An Evaluation of the Forecasting Performance of Three Econometric Models for the Eurozone and the USA," WIFO Working Papers 399, WIFO.
    5. Rocha, Jordano Vieira & Pereira, Pedro L. Valls, 2015. "Forecast comparison with nonlinear methods for Brazilian industrial production," Textos para discussão 397, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    6. Paula Bejarano Carbo, 2024. "The Nature of the Inflationary Surprise in Europe and the USA," National Institute of Economic and Social Research (NIESR) Discussion Papers 554, National Institute of Economic and Social Research.
    7. Katarina Juselius, 2011. "On the Role of Theory and Evidence in Macroeconomics," Chapters, in: John B. Davis & D. Wade Hands (ed.), The Elgar Companion to Recent Economic Methodology, chapter 17, Edward Elgar Publishing.
    8. Skrove Falch, Nina & Nymoen, Ragnar, 2011. "The accuracy of a forecast targeting central bank," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 5, pages 1-36.
    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. Fabio Bacchini & Cristina Brandimarte & Piero Crivelli & Roberta De Santis & Marco Fioramanti & Alessandro Girardi & Roberto Golinelli & Cecilia Jona-Lasinio & Massimo Mancini & Carmine Pappalardo & D, 2013. "Building the core of the Istat system of models for forecasting the Italian economy: MeMo-It," Rivista di statistica ufficiale, ISTAT - Italian National Institute of Statistics - (Rome, ITALY), vol. 15(1), pages 17-45.
    11. Ericsson Neil R., 2008. "Comment on "Economic Forecasting in a Changing World" (by Michael Clements and David Hendry)," Capitalism and Society, De Gruyter, vol. 3(2), pages 1-18, October.
    12. 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.
    13. Martínez-Ovando Juan Carlos & Walker Stephen G., 2011. "Time-series Modelling, Stationarity and Bayesian Nonparametric Methods," Working Papers 2011-08, Banco de México.
    14. Katarina Juselius, 2009. "Time to reject the privileging of economic theory over empirical evidence? A Reply to Lawson (2009)," Discussion Papers 09-16, University of Copenhagen. Department of Economics.
    15. David Hendry, 2010. "Climate Change: Lessons for our Future from the Distant Past," Economics Series Working Papers 485, University of Oxford, Department of Economics.
    16. Boris Salazar, 2016. "Mandelbrot, Fama and the emergence of econophysics," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, vol. 35(69), pages 637-662, April.
    17. Carl H. Korkpoe & Peterson Owusu Junior, 2018. "Behaviour of Johannesburg Stock Exchange All Share Index Returns - An Asymmetric GARCH and News Impact Effects Approach," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 68(1), pages 26-42, January-M.
    18. 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.

  45. Clements, Michael P. & Kim, Jae H., 2007. "Bootstrap prediction intervals for autoregressive time series," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3580-3594, April.

    Cited by:

    1. João Henrique Gonçalves Mazzeu & Esther Ruiz & Helena Veiga, 2018. "Uncertainty And Density Forecasts Of Arma Models: Comparison Of Asymptotic, Bayesian, And Bootstrap Procedures," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 388-419, April.
    2. Fang Yuan & Jiang Guo & Zhihuai Xiao & Bing Zeng & Wenqiang Zhu & Sixu Huang, 2020. "An Interval Forecasting Model Based on Phase Space Reconstruction and Weighted Least Squares Support Vector Machine for Time Series of Dissolved Gas Content in Transformer Oil," Energies, MDPI, vol. 13(7), pages 1-28, April.
    3. Alonso Fernández, Andrés Modesto & Bastos, Guadalupe & García-Martos, Carolina, 2017. "BIAS correction for dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS 24029, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    5. Mohitosh Kejriwal & Xuewen Yu, 2019. "Generalized Forecasr Averaging in Autoregressions with a Near Unit Root," Purdue University Economics Working Papers 1318, Purdue University, Department of Economics.
    6. Zi‐Yi Guo, 2021. "Out‐of‐sample performance of bias‐corrected estimators for diffusion processes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 243-268, March.
    7. Anna Staszewska-Bystrova, 2009. "Bootstrap Confidence Bands for Forecast Paths," Working Papers 024, COMISEF.
    8. Stephen Haben & Julien Caudron & Jake Verma, 2021. "Probabilistic Day-Ahead Wholesale Price Forecast: A Case Study in Great Britain," Forecasting, MDPI, vol. 3(3), pages 1-37, August.
    9. Bertail, Patrice & Clemencon, Stephan, 2008. "Approximate regenerative-block bootstrap for Markov chains," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2739-2756, January.
    10. Liu, Shen & Maharaj, Elizabeth Ann & Inder, Brett, 2014. "Polarization of forecast densities: A new approach to time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 345-361.
    11. Takada, Teruko, 2009. "Simulated minimum Hellinger distance estimation of stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2390-2403, April.
    12. 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.
    13. 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.
    14. Giovanni Fonseca & Federica Giummolè & Paolo Vidoni, 2021. "A note on simultaneous calibrated prediction intervals for time series," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 317-330, March.
    15. Alonso, Andres M. & Sipols, Ana E., 2008. "A time series bootstrap procedure for interpolation intervals," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1792-1805, January.
    16. 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.
    17. 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.
    18. Luisa Bisaglia & Margherita Gerolimetto, 2019. "Model-based INAR bootstrap for forecasting INAR(p) models," Computational Statistics, Springer, vol. 34(4), pages 1815-1848, December.
    19. Anna Staszewska-Bystrova & Peter Winker, 2016. "Improved bootstrap prediction intervals for SETAR models," Statistical Papers, Springer, vol. 57(1), pages 89-98, March.

  46. Fred Joutz & Michael P. Clements & Herman O. Stekler, 2007. "An evaluation of the forecasts of the federal reserve: a pooled approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 121-136.

    Cited by:

    1. Kenny, Geoff & Kostka, Thomas & Masera, Federico, 2013. "Can macroeconomists forecast risk? Event-based evidence from the euro area SPF," Working Paper Series 1540, European Central Bank.
    2. Sinclair, Tara M. & Gamber, Edward N. & Stekler, Herman & Reid, Elizabeth, 2012. "Jointly evaluating the Federal Reserve’s forecasts of GDP growth and inflation," International Journal of Forecasting, Elsevier, vol. 28(2), pages 309-314.
    3. Carola Conces Binder & Rodrigo Sekkel, 2023. "Central Bank Forecasting: A Survey," Staff Working Papers 23-18, Bank of Canada.
    4. Xiao, Jinzhi & Lence, Sergio H. & Hart, Chad E., 2015. "Do analysts forecast the ending stocks or the USDA forecasts?," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205759, Agricultural and Applied Economics Association.
    5. Paul Hubert, 2015. "Revisiting the greenbook's relative forecasting performance," Post-Print hal-01087522, HAL.
    6. Andrew C. Chang & Trace J. Levinson, 2023. "Raiders of the lost high‐frequency forecasts: New data and evidence on the efficiency of the Fed's forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 88-104, January.
    7. Eleonora Granziera & Pirkka Jalasjoki & Maritta Paloviita, 2021. "The Bias and Efficiency of the ECB Inflation Projections: a State Dependent Analysis," Working Paper 2021/1, Norges Bank.
    8. Tara Sinclair & Herman O. Stekler & Warren Carrow, 2012. "Evaluating a Vector of the Fed's Forecasts," Working Papers 2012-3, The George Washington University, Institute for International Economic Policy.
    9. Katharina Glass & Ulrich Fritsche, 2015. "Real-time Macroeconomic Data and Uncertainty," Macroeconomics and Finance Series 201406, University of Hamburg, Department of Socioeconomics.
    10. Paul Hubert, 2010. "Monetary policy, imperfect information and the expectations channel [Politique monétaire,information imparfaite et canal des anticipations]," SciencePo Working papers Main tel-04095385, HAL.
    11. Xiao, Jinzhi, 2015. "Essays on the forecasts of ending stocks," ISU General Staff Papers 201501010800005902, Iowa State University, Department of Economics.
    12. Dean Croushore & Simon van Norden, 2017. "Fiscal Surprises at the FOMC," CIRANO Working Papers 2017s-09, CIRANO.
    13. Ager, Philipp & Kappler, Marcus & Osterloh, Steffen, 2007. "The Accuracy and Efficiency of the Consensus Forecasts: A Further Application and Extension of the Pooled Approach," ZEW Discussion Papers 07-058, ZEW - Leibniz Centre for European Economic Research.
    14. Chen, Qiwei & Costantini, Mauro & Deschamps, Bruno, 2016. "How accurate are professional forecasts in Asia? Evidence from ten countries," International Journal of Forecasting, Elsevier, vol. 32(1), pages 154-167.
    15. Jeff Messina & Tara M. Sinclair & Herman O. Stekler, 2014. "What Can We Learn From Revisions To The Greenbook Forecasts?," Working Papers 2014-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    16. Strunz, Franziska & Gödl, Maximilian, 2023. "An Evaluation of Professional Forecasts for the German Economy," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277707, Verein für Socialpolitik / German Economic Association.
    17. Demetrescu, Matei & Hacioglu Hoke, Sinem, 2018. "Predictive regressions under asymmetric loss: factor augmentation and model selection," Bank of England working papers 723, Bank of England.
    18. 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.
    19. Silvija Vlah Jerić & Mihovil Anđelinović, 2019. "Evaluating Croatian stock index forecasts," Empirical Economics, Springer, vol. 56(4), pages 1325-1339, April.
    20. Travis J. Berge & Andrew C. Chang & Nitish R. Sinha, 2019. "Evaluating the Conditionality of Judgmental Forecasts," Finance and Economics Discussion Series 2019-002, Board of Governors of the Federal Reserve System (U.S.).
    21. Arai, Natsuki, 2014. "Using forecast evaluation to improve the accuracy of the Greenbook forecast," International Journal of Forecasting, Elsevier, vol. 30(1), pages 12-19.
    22. Mihaela Simionescu, 2014. "Directional accuracy for inflation and unemployment rate predictions in Romania," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 7(2), pages 129-138, September.
    23. Papastamos, Dimitrios & Matysiak, George & Stevenson, Simon, 2015. "Assessing the accuracy and dispersion of real estate investment forecasts," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 141-152.
    24. Paul Hubert, 2009. "Informational Advantage and Influence of Communicating Central Banks," Documents de Travail de l'OFCE 2009-04, Observatoire Francais des Conjonctures Economiques (OFCE).
    25. Jonas Dovern & Johannes Weisser, 2009. "Accuracy, Unbiasedness and Efficiency of Professional Macroeconomic Forecasts: An empirical Comparison for the G7," Jena Economics Research Papers 2009-091, Friedrich-Schiller-University Jena.
    26. Kajal Lahiri, 2012. "Comment on "Forecast Rationality Tests Based on Multi-Horizon Bounds" by Andrew Patton and Allan Timmermann. Journal of Business and Economic Statistics, No. 1, Vol. 30, 2012, pp.1-17," Discussion Papers 12-10, University at Albany, SUNY, Department of Economics.
    27. 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.
    28. Sun, Yuying & Wang, Shouyang & Zhang, Xun, 2018. "How efficient are China's macroeconomic forecasts? Evidences from a new forecasting evaluation approach," Economic Modelling, Elsevier, vol. 68(C), pages 506-513.
    29. Mihaela SIMIONESCU, 2015. "The Evaluation of Global Accuracy of Romanian Inflation Rate Predictions Using Mahalanobis Distance," Management Dynamics in the Knowledge Economy, College of Management, National University of Political Studies and Public Administration, vol. 3(1), pages 133-149, March.
    30. Dimitrios Papastamos & George Matysiak & Simon Stevenson, 2014. "A Comparative Analysis of the Accuracy and Uncertainty in Real Estate and Macroeconomic Forecasts," Real Estate & Planning Working Papers rep-wp2014-06, Henley Business School, University of Reading.
    31. Engelke, Carola & Heinisch, Katja & Schult, Christoph, 2019. "How forecast accuracy depends on conditioning assumptions," IWH Discussion Papers 18/2019, Halle Institute for Economic Research (IWH).
    32. Lillian R. Gaeto & Sandeep Mazumder, 2019. "Measuring the Accuracy of Federal Reserve Forecasts," Southern Economic Journal, John Wiley & Sons, vol. 85(3), pages 960-984, January.
    33. Granziera, Eleonora & Jalasjoki, Pirkka & Paloviita, Maritta, 2021. "The bias and efficiency of the ECB inflation projections: A state dependent analysis," Bank of Finland Research Discussion Papers 7/2021, Bank of Finland.
    34. Sheng, Xuguang (Simon), 2015. "Evaluating the economic forecasts of FOMC members," International Journal of Forecasting, Elsevier, vol. 31(1), pages 165-175.
    35. Paul Hubert, 2010. "Monetary Policy, Imperfect Information and the Expectations Channel," Sciences Po publications info:hdl:2441/f4rshpf3v1u, Sciences Po.
    36. Kontogeorgos, Georgios & Lambrias, Kyriacos, 2019. "An analysis of the Eurosystem/ECB projections," Working Paper Series 2291, European Central Bank.
    37. Dovern, Jonas & Weisser, Johannes, 2008. "Are they really rational? Assessing professional macro-economic forecasts from the G7-countries," Kiel Working Papers 1447, Kiel Institute for the World Economy (IfW Kiel).
    38. Peter Tillmann, 2011. "Reputation and Forecast Revisions: Evidence from the FOMC," MAGKS Papers on Economics 201128, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    39. 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.
    40. Davies, Antony, 2006. "A framework for decomposing shocks and measuring volatilities derived from multi-dimensional panel data of survey forecasts," International Journal of Forecasting, Elsevier, vol. 22(2), pages 373-393.
    41. Binder, Carola Conces & Wetzel, Samantha, 2018. "The FOMC versus the staff, revisited: When do policymakers add value?," Economics Letters, Elsevier, vol. 171(C), pages 72-75.
    42. Deschamps, Bruno & Ioannidis, Christos, 2013. "Can rational stubbornness explain forecast biases?," Journal of Economic Behavior & Organization, Elsevier, vol. 92(C), pages 141-151.
    43. Xiao, Jinzhi & Lence, Sergio H. & Hart, Chad, 2014. "Usda And Private Analysts' Forecasts Of Ending Stocks: How Good Are They?," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170642, Agricultural and Applied Economics Association.
    44. Jones, Adam T. & Ogden, Richard E., 2017. "A day late and a dollar short: The effect of policy uncertainty on fed forecast errors," Economic Analysis and Policy, Elsevier, vol. 54(C), pages 112-122.
    45. Paul Hubert, 2015. "Do Central Bank forecasts influence private agents? Forecasting Performance vs. Signals," Post-Print hal-03399242, HAL.
    46. Sergey V. Smirnov, 2014. "Predicting US Recessions: Does a Wishful Bias Exist?," HSE Working papers WP BRP 77/EC/2014, National Research University Higher School of Economics.
    47. Kappler, Marcus, 2007. "Projecting the Medium-Term: Outcomes and Errors for GDP Growth," ZEW Discussion Papers 07-068, ZEW - Leibniz Centre for European Economic Research.
    48. Vereda, Luciano & Savignon, João & Gouveia da Silva, Tarciso, 2021. "A new method to assess the degree of information rigidity using fixed-event forecasts," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1576-1589.

  47. Michael Clements, 2006. "Evaluating the survey of professional forecasters probability distributions of expected inflation based on derived event probability forecasts," Empirical Economics, Springer, vol. 31(1), pages 49-64, March.

    Cited by:

    1. Kenny, Geoff & Kostka, Thomas & Masera, Federico, 2013. "Can macroeconomists forecast risk? Event-based evidence from the euro area SPF," Working Paper Series 1540, European Central Bank.
    2. Geweke, John & Amisano, Gianni, 2011. "Optimal prediction pools," Journal of Econometrics, Elsevier, vol. 164(1), pages 130-141, September.
    3. Hasumi, Ryo & Iiboshi, Hirokuni & Matsumae, Tatsuyoshi & Nakamura, Daisuke, 2019. "Does a financial accelerator improve forecasts during financial crises? Evidence from Japan with prediction-pooling methods," Journal of Asian Economics, Elsevier, vol. 60(C), pages 45-68.
    4. Alexander Glas & Matthias Hartmann, 2022. "Uncertainty measures from partially rounded probabilistic forecast surveys," Quantitative Economics, Econometric Society, vol. 13(3), pages 979-1022, July.
    5. Geoff Kenny & Thomas Kostka & Federico Masera, 2015. "Density characteristics and density forecast performance: a panel analysis," Empirical Economics, Springer, vol. 48(3), pages 1203-1231, May.
    6. Tsyplakov, Alexander, 2014. "Theoretical guidelines for a partially informed forecast examiner," MPRA Paper 55017, University Library of Munich, Germany.
    7. Garratt, Anthony & Lee, Kevin & Shields, Kalvinder, 2016. "Forecasting global recessions in a GVAR model of actual and expected output," International Journal of Forecasting, Elsevier, vol. 32(2), pages 374-390.
    8. Clements, Michael P., 2006. "Internal consistency of survey respondentsíforecasts: Evidence based on the Survey of Professional Forecasters," Economic Research Papers 269742, University of Warwick - Department of Economics.
    9. Bedri Kamil Onur Taş, 2016. "Does the Federal Reserve have Private Information about its Future Actions?," Economica, London School of Economics and Political Science, vol. 83(331), pages 498-517, July.
    10. Clements, Michael P., 2008. "Rounding of probability forecasts: The SPF forecast probabilities of negative output growth," Economic Research Papers 269880, University of Warwick - Department of Economics.
    11. 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.
    12. Wojciech Charemza & Carlos Diaz Vela & Svetlana Makarova, 2013. "Inflation fan charts, monetary policy and skew normal distribution," Discussion Papers in Economics 13/06, Division of Economics, School of Business, University of Leicester.
    13. Christian Kascha & Francesco Ravazzolo, 2008. "Combining inflation density forecasts," Working Paper 2008/22, Norges Bank.
    14. J. James Reade & Carl Singleton & Alasdair Brown, 2021. "Evaluating strange forecasts: The curious case of football match scorelines," Scottish Journal of Political Economy, Scottish Economic Society, vol. 68(2), pages 261-285, May.
    15. Clements, Michael P, 2012. "Subjective and Ex Post Forecast Uncertainty : US Inflation and Output Growth," The Warwick Economics Research Paper Series (TWERPS) 995, University of Warwick, Department of Economics.
    16. Yousung Park & Hee-Young Kim, 2012. "Diagnostic checks for integer-valued autoregressive models using expected residuals," Statistical Papers, Springer, vol. 53(4), pages 951-970, November.
    17. Michael P. Clements, 2011. "An Empirical Investigation of the Effects of Rounding on the SPF Probabilities of Decline and Output Growth Histograms," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(1), pages 207-220, February.
    18. 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.
    19. Clements, Michael P., 2008. "Consensus and uncertainty: Using forecast probabilities of output declines," International Journal of Forecasting, Elsevier, vol. 24(1), pages 76-86.
    20. Hall, Stephen G. & Mitchell, James, 2007. "Combining density forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 1-13.
    21. Anthony Garratt & Kevin Lee & Kalvinder Shields, 2014. "Forecasting Global Recessions in a GVAR Model of Actual and Expected Output in the G7," Discussion Papers 2014/06, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).

  48. Michael P. Clements & David F. Hendry, 2005. "Evaluating a Model by Forecast Performance," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 931-956, December.

    Cited by:

    1. 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.
    2. Stekler, H.O., 2007. "The future of macroeconomic forecasting: Understanding the forecasting process," International Journal of Forecasting, Elsevier, vol. 23(2), pages 237-248.
    3. David E. Bloom & David Canning & Günther Fink & Jocelyn E. Finlay, 2007. "Does Age Structure Forecast Economic Growth?," NBER Working Papers 13221, National Bureau of Economic Research, Inc.
    4. Bauwens, Luc & Sucarrat, Genaro, 2008. "General to specific modelling of exchange rate volatility : a forecast evaluation," UC3M Working papers. Economics we081810, Universidad Carlos III de Madrid. Departamento de Economía.
    5. Michael Funke & Aaron Mehrotra & Hao Yu, 2015. "Tracking Chinese CPI inflation in real time," Empirical Economics, Springer, vol. 48(4), pages 1619-1641, June.
    6. Patrick Minford & Yongdeng Xu & Peng Zhou, 2015. "How Good are Out of Sample Forecasting Tests on DSGE Models?," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 1(3), pages 333-351, November.
    7. Odeck, James & Welde, Morten, 2017. "The accuracy of toll road traffic forecasts: An econometric evaluation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 101(C), pages 73-85.
    8. Clements, Michael P., 2016. "Real-time factor model forecasting and the effects of instability," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 661-675.
    9. 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.
    10. 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.
    11. Clements, Michael P. & Reade, J. James, 2020. "Forecasting and forecast narratives: The Bank of England Inflation Reports," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1488-1500.
    12. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2013. "Forecasting by factors, by variables, by both or neither?," Journal of Econometrics, Elsevier, vol. 177(2), pages 305-319.

  49. Michael P. Clements & David F. Hendry, 2005. "Guest Editors’ Introduction: Information in Economic Forecasting," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(s1), pages 713-753, December.

    Cited by:

    1. Ericsson, Neil R., 2017. "Economic forecasting in theory and practice: An interview with David F. Hendry," International Journal of Forecasting, Elsevier, vol. 33(2), pages 523-542.
    2. Propper, Carol & Janke, Katharina & Lee, Kevin & Shields, Kalvinder & Shields, Michael A, 2020. "Macroeconomic Conditions and Health in Britain: Aggregation, Dynamics and Local Area Heterogeneity," CEPR Discussion Papers 14507, C.E.P.R. Discussion Papers.
    3. Guillaume Chevillon, 2007. "Direct Multi‐Step Estimation And Forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 746-785, September.
    4. Jennifer Castle & David Hendry & Nicholas W.P. Fawcett, 2008. "Forecasting with Equilibrium-correction Models during Structural Breaks," Economics Series Working Papers 408, University of Oxford, Department of Economics.
    5. 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).
    6. Janke, Katharina & Lee, Kevin & Propper, Carol & Shields, Kalvinder & Shields, Michael A., 2023. "Economic conditions and health: Local effects, national effect and local area heterogeneity," Journal of Economic Behavior & Organization, Elsevier, vol. 214(C), pages 801-828.
    7. Georgios Papadopoulos & Dionysios Chionis & Nikolaos P. Rachaniotis, 2018. "Macro-financial linkages during tranquil and crisis periods: evidence from stressed economies," Risk Management, Palgrave Macmillan, vol. 20(2), pages 142-166, May.
    8. Nilss Olekalns & Kalvinder Shields, 2008. "Nowcasting, Business Cycle Dating and the Interpretation of New Information when Real Time Data are Available," Department of Economics - Working Papers Series 1040, The University of Melbourne.
    9. Jennifer Castle & David Hendry, 2008. "The Long-Run Determinants of UK Wages, 1860-2004," Economics Series Working Papers 409, University of Oxford, Department of Economics.
    10. David F. Hendry & Grayham E. Mizon, 2013. "Unpredictability in Economic Analysis, Econometric Modeling and Forecasting," Economics Papers 2013-W04, Economics Group, Nuffield College, University of Oxford.
    11. 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.
    12. Ahumada, Hildegart A. & Garegnani, Maria Lorena, 2012. "Forecasting a monetary aggregate under instability: Argentina after 2001," International Journal of Forecasting, Elsevier, vol. 28(2), pages 412-427.
    13. 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.
    14. Jennifer Castle & David Hendry, 2007. "Forecasting UK Inflation: the Roles of Structural Breaks and Time Disaggregation," Economics Series Working Papers 309, University of Oxford, Department of Economics.

  50. David F. Hendry & Michael P. Clements, 2004. "Pooling of forecasts," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 1-31, June.
    See citations under working paper version above.
  51. Michael P. Clements & Hans-Martin Krolzig, 2004. "Can regime-switching models reproduce the business cycle features of US aggregate consumption, investment and output?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 9(1), pages 1-14.

    Cited by:

    1. Marlon Fritz & Thomas Gries & Yuanhua Feng, 2019. "Growth Trends and Systematic Patterns of Booms and Busts‐Testing 200 Years of Business Cycle Dynamics," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(1), pages 62-78, February.
    2. Morley James & Piger Jeremy & Tien Pao-Lin, 2013. "Reproducing business cycle features: are nonlinear dynamics a proxy for multivariate information?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(5), pages 483-498, December.
    3. Panayotis G. Michaelides & Efthymios G. Tsionas & Angelos T. Vouldis & Konstantinos N. Konstantakis & Panagiotis Patrinos, 2018. "A Semi-Parametric Non-linear Neural Network Filter: Theory and Empirical Evidence," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 637-675, March.
    4. George Athanasopoulos & Heather M. Anderson & Farshid Vahid, 2007. "Nonlinear autoregressive leading indicator models of output in G-7 countries," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 63-87.
    5. Balcilar, Mehmet & Kutan, Ali M. & Yaya, Mehmet E., 2017. "Testing the dependency theory on small island economies: The case of Cyprus," Economic Modelling, Elsevier, vol. 61(C), pages 1-11.
    6. James Morley & Jeremy M. Piger, 2005. "The importance of nonlinearity in reproducing business cycle features," Working Papers 2004-032, Federal Reserve Bank of St. Louis.
    7. James Morley & Jeremy Piger & Pao-Lin Tien, 2009. "Reproducing Business Cycle Features: How Important Is Nonlinearity Versus Multivariate Information?," Wesleyan Economics Working Papers 2009-003, Wesleyan University, Department of Economics.
    8. 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.

  52. Clements, Michael P. & Galvao, Ana Beatriz, 2004. "A comparison of tests of nonlinear cointegration with application to the predictability of US interest rates using the term structure," International Journal of Forecasting, Elsevier, vol. 20(2), pages 219-236.

    Cited by:

    1. Pär Österholm, 2008. "Can forecasting performance be improved by considering the steady state? An application to Swedish inflation and interest rate," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(1), pages 41-51.
    2. David Hendry & Andrew B. Martinez, 2016. "Evaluating Multi-Step System Forecasts with Relatively Few Forecast-Error Observations," Economics Series Working Papers 784, University of Oxford, Department of Economics.
    3. 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.
    4. Ali Taiebnia & Shapour Mohammadi, 2023. "Forecast accuracy of the linear and nonlinear autoregressive models in macroeconomic modeling," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2045-2062, December.
    5. Alessio Anzuini, 2020. "The non-linear effects of the Fed's asset purchases," Temi di discussione (Economic working papers) 1280, Bank of Italy, Economic Research and International Relations Area.
    6. Frédérique Bec & Anders Rahbek, 2004. "Vector equilibrium correction models with non-linear discontinuous adjustments," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 628-651, December.
    7. Aslanidis, Nektarios, 2007. "Business Cycle Regimes in CEECs Production: A Threshold SURE Approach," Working Papers 2072/5318, Universitat Rovira i Virgili, Department of Economics.
    8. Goodell, John W. & McGroarty, Frank & Urquhart, Andrew, 2015. "Political uncertainty and the 2012 US presidential election: A cointegration study of prediction markets, polls and a stand-out expert," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 162-171.
    9. Sugita, Katsuhiro & 杉田, 勝弘, 2006. "Time Series Analysis of the Expectations Hypothesis for the Japanese Term Structure of Interest Rates in the Presence of Multiple Structural Breaks," Discussion Papers 2006-15, Graduate School of Economics, Hitotsubashi University.
    10. Jan G. De Gooijer & Antoni Vidiella-i-Anguera, 2005. "Estimating threshold cointegrated systems," Economics Bulletin, AccessEcon, vol. 3(8), pages 1-7.
    11. West, Kenneth D., 2006. "Forecast Evaluation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 3, pages 99-134, Elsevier.
    12. Nicholas Apergis & Emmanuel Mamatzakis & Christos Staikouras, 2011. "Testing for Regime Changes in Greek Sovereign Debt Crisis," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 17(3), pages 258-273, August.
    13. Ana Beatriz C. Galvão, 2006. "Structural break threshold VARs for predicting US recessions using the spread," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(4), pages 463-487, May.
    14. Topal, Pinar, 2015. "Fiscal stimulus and labor market flexibility," SAFE Working Paper Series 90, Leibniz Institute for Financial Research SAFE.
    15. Dalu Zhang & Peter Moffatt, 2013. "Time series non-linearity in the real growth / recession-term spread relationship," University of East Anglia Applied and Financial Economics Working Paper Series 047, School of Economics, University of East Anglia, Norwich, UK..
    16. Tommaso Ferraresi & Andrea Roventini & Giorgio Fagiolo, 2015. "Fiscal Policies and Credit Regimes: A TVAR Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(7), pages 1047-1072, November.
    17. Clements, Michael P. & Galvão, Ana Beatriz C., 2003. "Testing The Expectations Theory Of The Term Structure Of Interest Rates In Threshold Models," Macroeconomic Dynamics, Cambridge University Press, vol. 7(4), pages 567-585, September.
    18. Alessio Anzuini & Francesca Brusa, 2016. "Carry trades and exchange rate volatility: a TVAR approach," Temi di discussione (Economic working papers) 1046, Bank of Italy, Economic Research and International Relations Area.
    19. John Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Staff Working Papers 07-1, Bank of Canada.

  53. Clements, Michael P. & Franses, Philip Hans & Swanson, Norman R., 2004. "Forecasting economic and financial time-series with non-linear models," International Journal of Forecasting, Elsevier, vol. 20(2), pages 169-183.
    See citations under working paper version above.
  54. Michael P. Clements, 2004. "Evaluating the Bank of England Density Forecasts of Inflation," Economic Journal, Royal Economic Society, vol. 114(498), pages 844-866, October.

    Cited by:

    1. Lundholm, Michael, 2010. "Sveriges Riksbank's Inflation Interval Forecasts 1999-2005," Research Papers in Economics 2010:11, Stockholm University, Department of Economics.
    2. Clements, Michael P., 2008. "Explanations of the inconsistencies in survey respondents'forecasts," The Warwick Economics Research Paper Series (TWERPS) 870, University of Warwick, Department of Economics.
    3. Gergely Ganics & Barbara Rossi & Tatevik Sekhposyan, 2019. "From fixed-event to fixed-horizon density forecasts: Obtaining measures of multi-horizon uncertainty from survey density forecasts," Economics Working Papers 1689, Department of Economics and Business, Universitat Pompeu Fabra.
    4. 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.
    5. Michael Clements, 2016. "Are Macroeconomic Density Forecasts Informative?," ICMA Centre Discussion Papers in Finance icma-dp2016-02, Henley Business School, University of Reading.
    6. Sinclair, Tara M. & Gamber, Edward N. & Stekler, Herman & Reid, Elizabeth, 2012. "Jointly evaluating the Federal Reserve’s forecasts of GDP growth and inflation," International Journal of Forecasting, Elsevier, vol. 28(2), pages 309-314.
    7. Carola Conces Binder & Rodrigo Sekkel, 2023. "Central Bank Forecasting: A Survey," Staff Working Papers 23-18, Bank of Canada.
    8. Jacob T. Jones & Tara M. Sinclair & Herman O. Stekler, 2018. "A Textual Analysis of the Bank of England Growth Forecasts," Working Papers 2018-005, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting, revised May 2019.
    9. Barbara Rossi & Tatevik Sekhposyan, 2015. "Alternative Tests for Correct Specification of Conditional Predictive Densities," Working Papers 758, Barcelona School of Economics.
    10. Knüppel, Malte & Schultefrankenfeld, Guido, 2019. "Assessing the uncertainty in central banks’ inflation outlooks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1748-1769.
    11. David Johnstone, 2007. "Economic Darwinism: Who has the Best Probabilities?," Theory and Decision, Springer, vol. 62(1), pages 47-96, February.
    12. Gian Luigi Mazzi & James Mitchell & Gaetana Montana, 2014. "Density Nowcasts and Model Combination: Nowcasting Euro-Area GDP Growth over the 2008–09 Recession," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(2), pages 233-256, April.
    13. Christopher McDonald & Craig Thamotheram & Shaun P. Vahey & Elizabeth C. Wakerly, 2016. "Assessing the economic value of probabilistic forecasts in the presence of an inflation target," CAMA Working Papers 2016-40, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    14. Pär Österholm, 2006. "Incorporating judgement in fan charts," Finance and Economics Discussion Series 2006-39, Board of Governors of the Federal Reserve System (U.S.).
    15. Casillas-Olvera, Gabriel & Bessler, David A., 2006. "Probability forecasting and central bank accountability," Journal of Policy Modeling, Elsevier, vol. 28(2), pages 223-234, February.
    16. Pao-Lin Tien & Tara M. Sinclair & Edward N. Gamber, 2016. "Do Fed Forecast Errors Matter?," Working Papers 2016-007, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    17. D. J. Johnstone & S. Jones & V. R. R. Jose & M. Peat, 2013. "Measures of the economic value of probabilities of bankruptcy," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(3), pages 635-653, June.
    18. 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.
    19. Wojciech Charemza & Carlos Diaz Vela & Svetlana Makarova, 2013. "Too many skew normal distributions? The practitioner’s perspective," Discussion Papers in Economics 13/07, Division of Economics, School of Business, University of Leicester.
    20. Dr. James Mitchell, 2009. "Macro Modelling with Many Models," National Institute of Economic and Social Research (NIESR) Discussion Papers 337, National Institute of Economic and Social Research.
    21. Ana Beatriz Galvão & James Mitchell, 2019. "Measuring Data Uncertainty: An Application using the Bank of England's "Fan Charts" for Historical GDP Growth," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2019-08, Economic Statistics Centre of Excellence (ESCoE).
    22. Tsyplakov, Alexander, 2013. "Evaluation of Probabilistic Forecasts: Proper Scoring Rules and Moments," MPRA Paper 45186, University Library of Munich, Germany.
    23. Ornela SHALARI & Fejzi KOLANECI, 2014. "Statistical analysis of the inflation in the case of Albania," EuroEconomica, Danubius University of Galati, issue 2(33), pages 67-77, November.
    24. Gianna Boero & Jeremy Smith & Kenneth F. Wallis, 2008. "Uncertainty and Disagreement in Economic Prediction: The Bank of England Survey of External Forecasters," Economic Journal, Royal Economic Society, vol. 118(530), pages 1107-1127, July.
    25. Rossi, Barbara & Ganics, Gergely & Sekhposyan, Tatevik, 2020. "From Fixed-event to Fixed-horizon Density Forecasts: Obtaining Measures of Multi-horizon Uncertainty from Survey Density Foreca," CEPR Discussion Papers 14267, C.E.P.R. Discussion Papers.
    26. Wolden Bache, Ida & Sofie Jore, Anne & Mitchell, James & Vahey, Shaun P., 2011. "Combining VAR and DSGE forecast densities," Journal of Economic Dynamics and Control, Elsevier, vol. 35(10), pages 1659-1670, October.
    27. Eva Regnier, 2018. "Probability Forecasts Made at Multiple Lead Times," Management Science, INFORMS, vol. 64(5), pages 2407-2426, May.
    28. Ana Beatriz Galvão & James Mitchell, 2023. "Real‐Time Perceptions of Historical GDP Data Uncertainty," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 457-481, June.
    29. Wagner Piazza Gaglianone & Luiz Renato Lima, 2012. "Constructing Density Forecasts from Quantile Regressions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(8), pages 1589-1607, December.
    30. John W. Galbraith & Simon van Norden, 2009. "Calibration and Resolution Diagnostics for Bank of England Density Forecasts," CIRANO Working Papers 2009s-36, CIRANO.
    31. Shaun Vahey & Tony Garratt, 2005. "UK Real-time Macro Data Characteristics," Computing in Economics and Finance 2005 253, Society for Computational Economics.
    32. Knut Are Aastveit & James Mitchell & Francesco Ravazzolo & Herman van Dijk, 2018. "The Evolution of Forecast Density Combinations in Economics," Tinbergen Institute Discussion Papers 18-069/III, Tinbergen Institute.
    33. Rodríguez, Gabriel, 2017. "Modeling Latin-American stock and Forex markets volatility: Empirical application of a model with random level shifts and genuine long memory," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 393-420.
    34. Wojciech Charemza & Carlos Diaz Vela & Svetlana Makarova, 2013. "Inflation fan charts, monetary policy and skew normal distribution," Discussion Papers in Economics 13/06, Division of Economics, School of Business, University of Leicester.
    35. Galvao, Ana Beatriz, 2016. "Data Revisions and DSGE Models," EMF Research Papers 11, Economic Modelling and Forecasting Group.
    36. Jackson, Emerson Abraham & Tamuke, Edmund, 2018. "Probability Forecast Using Fan Chart Analysis: A case of the Sierra Leone Economy," MPRA Paper 88853, University Library of Munich, Germany, revised 04 Sep 2018.
    37. Robert W. Rich & Joseph Tracy, 2017. "The behavior of uncertainty and disagreement and their roles in economic prediction: a panel analysis," Staff Reports 808, Federal Reserve Bank of New York.
    38. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2008. "Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails," Discussion Papers 2008-10, School of Economics, The University of New South Wales.
    39. Carriero, Andrea & Galvão, Ana Beatriz & Kapetanios, George, 2019. "A comprehensive evaluation of macroeconomic forecasting methods," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1226-1239.
    40. Knüppel, Malte, 2011. "Evaluating the calibration of multi-step-ahead density forecasts using raw moments," Discussion Paper Series 1: Economic Studies 2011,32, Deutsche Bundesbank.
    41. Anne Sofie Jore & James Mitchell & Shaun Vahey, 2008. "Combining Forecast Densities from VARs with Uncertain Instabilities," Reserve Bank of New Zealand Discussion Paper Series DP2008/18, Reserve Bank of New Zealand.
    42. Fabio Busetti, 2017. "Quantile Aggregation of Density Forecasts," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 79(4), pages 495-512, August.
    43. Christian Kascha & Francesco Ravazzolo, 2008. "Combining inflation density forecasts," Working Paper 2008/22, Norges Bank.
    44. Herman O. Stekler, 2008. "What Do We Know About G-7 Macro Forecasts?," Working Papers 2008-009, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    45. J. James Reade & Carl Singleton & Alasdair Brown, 2021. "Evaluating strange forecasts: The curious case of football match scorelines," Scottish Journal of Political Economy, Scottish Economic Society, vol. 68(2), pages 261-285, May.
    46. Gneiting, Tilmann, 2011. "Quantiles as optimal point forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 197-207.
    47. Kevin Dowd, 2004. "The Swedish Inflation Fan Charts: An Evaluation of the Riksbank?s Inflation Density Forecasts," Occasional Papers 10, Industrial Economics Division, revised 11 Jan 2004.
    48. Jorge Fornero & Andrés Gatty, 2020. "Back testing fan charts of activity and inflation: the Chilean case," Working Papers Central Bank of Chile 881, Central Bank of Chile.
    49. 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.
    50. Tsyplakov, Alexander, 2012. "Assessment of probabilistic forecasts: Proper scoring rules and moments," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 27(3), pages 115-132.
    51. Knüppel, Malte & Schultefrankenfeld, Guido, 2008. "How informative are macroeconomic risk forecasts? An examination of the Bank of England's inflation forecasts," Discussion Paper Series 1: Economic Studies 2008,14, Deutsche Bundesbank.
    52. Faust, Jon & Wright, Jonathan H., 2013. "Forecasting Inflation," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 2-56, Elsevier.
    53. Poitras, Geoffrey, 2006. "More on the correct use of omnibus tests for normality," Economics Letters, Elsevier, vol. 90(3), pages 304-309, March.
    54. Marc-Oliver Pohle, 2020. "The Murphy Decomposition and the Calibration-Resolution Principle: A New Perspective on Forecast Evaluation," Papers 2005.01835, arXiv.org.
    55. Clements, Michael P. & Reade, J. James, 2020. "Forecasting and forecast narratives: The Bank of England Inflation Reports," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1488-1500.
    56. Rui Fan & Stephen J. Taylor & Matteo Sandri, 2018. "Density forecast comparisons for stock prices, obtained from high‐frequency returns and daily option prices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(1), pages 83-103, January.
    57. Dick van Dijk & Haris Munandar & Christian M. Hafner, 2005. "The Euro Introduction and Non-Euro Currencies," Tinbergen Institute Discussion Papers 05-044/4, Tinbergen Institute, revised 08 Jun 2006.
    58. David J. Johnstone, 2007. "The Parimutuel Kelly Probability Scoring Rule," Decision Analysis, INFORMS, vol. 4(2), pages 66-75, June.
    59. Hall, Stephen G. & Mitchell, James, 2007. "Combining density forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 1-13.
    60. Gneiting, Tilmann, 2011. "Quantiles as optimal point forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 197-207, April.
    61. Goodhart, Charles, 2005. "An essay on the interactions between the Bank of England's forecasts, the MPC's policy adjustments, and the eventual outcome," LSE Research Online Documents on Economics 24665, London School of Economics and Political Science, LSE Library.
    62. 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.
    63. Diks, Cees & Panchenko, Valentyn & van Dijk, Dick, 2011. "Likelihood-based scoring rules for comparing density forecasts in tails," Journal of Econometrics, Elsevier, vol. 163(2), pages 215-230, August.
    64. Goodhart Charles A.E., 2005. "The Monetary Policy Committee's Reaction Function: An Exercise in Estimation," The B.E. Journal of Macroeconomics, De Gruyter, vol. 5(1), pages 1-42, August.
    65. Grant, Andrew & Johnstone, David, 2010. "Finding profitable forecast combinations using probability scoring rules," International Journal of Forecasting, Elsevier, vol. 26(3), pages 498-510, July.
    66. Par Osterholm, 2008. "A structural Bayesian VAR for model-based fan charts," Applied Economics, Taylor & Francis Journals, vol. 40(12), pages 1557-1569.
    67. Michal Franta & Jozef Baruník & Roman Horváth & Katerina Smídková, 2014. "Are Bayesian Fan Charts Useful? The Effect of Zero Lower Bound and Evaluation of Financial Stability Stress Tests," International Journal of Central Banking, International Journal of Central Banking, vol. 10(1), pages 159-188, March.
    68. Tsuchiya, Yoichi, 2022. "Evaluating the European Central Bank’s uncertainty forecasts," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 321-330.
    69. Taylor, James W., 2020. "A strategic predictive distribution for tests of probabilistic calibration," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1380-1388.
    70. Funke, Michael & Loermann, Julius & Tsang, Andrew, 2017. "The information content in the offshore Renminbi foreign-exchange option market: Analytics and implied USD/CNH densities," BOFIT Discussion Papers 15/2017, Bank of Finland Institute for Emerging Economies (BOFIT).

  55. Michael P. Clements & Nick Taylor, 2003. "Evaluating interval forecasts of high-frequency financial data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 445-456.

    Cited by:

    1. Yushu Li & Jonas Andersson, 2020. "A likelihood ratio and Markov chain‐based method to evaluate density forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 47-55, January.
    2. Rafal Weron, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," HSC Research Reports HSC/14/07, Hugo Steinhaus Center, Wroclaw University of Technology.
    3. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    4. Wallis, Kenneth F., 2002. "Chi-squared tests of interval and density forecasts, and the Bank of England's fan charts," Royal Economic Society Annual Conference 2002 181, Royal Economic Society.
    5. Taylor, Nicholas, 2007. "A note on the importance of overnight information in risk management models," Journal of Banking & Finance, Elsevier, vol. 31(1), pages 161-180, January.
    6. Tsyplakov, Alexander, 2014. "Theoretical guidelines for a partially informed forecast examiner," MPRA Paper 55017, University Library of Munich, Germany.
    7. T.S. Tuang Buansing & Amos Golan & Aman Ullah, 2019. "Information-Theoretic Approach for Forecasting Interval-Valued SP500 Daily Returns," Working Papers 201922, University of California at Riverside, Department of Economics.
    8. Storti, G., 2006. "Minimum distance estimation of GARCH(1,1) models," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1803-1821, December.
    9. Tsyplakov, Alexander, 2013. "Evaluation of Probabilistic Forecasts: Proper Scoring Rules and Moments," MPRA Paper 45186, University Library of Munich, Germany.
    10. Carol Alexander & Emese Lazar & Silvia Stanescu, 2011. "Analytic Approximations to GARCH Aggregated Returns Distributions with Applications to VaR and ETL," ICMA Centre Discussion Papers in Finance icma-dp2011-08, Henley Business School, University of Reading.
    11. Dominique, C-Rene, 2013. "Estimating investors' behavior and errors in probabilistic forecasts by the Kolmogorov entropy and noise colors of non-hyperbolic attractors," MPRA Paper 46451, University Library of Munich, Germany.
    12. Maciejowska, Katarzyna & Nowotarski, Jakub & Weron, Rafał, 2016. "Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging," International Journal of Forecasting, Elsevier, vol. 32(3), pages 957-965.
    13. Kolassa, Stephan, 2016. "Evaluating predictive count data distributions in retail sales forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 788-803.
    14. Helmut Herwartz & Israel Waichman, 2010. "A comparison of bootstrap and Monte-Carlo testing approaches to value-at-risk diagnosis," Computational Statistics, Springer, vol. 25(4), pages 725-732, December.
    15. Elena-Ivona DUMITRESCU & Christophe HURLIN & Jaouad MADKOUR, 2011. "Testing Interval Forecasts: A New GMM-based Test," LEO Working Papers / DR LEO 1549, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    16. Mauricio Lopera & Ramón Javier Mesa & Charle Londoño, 2014. "Evaluando las intervenciones cambiarias en Colombia: 2004-2012," Estudios Gerenciales, Universidad Icesi, March.
    17. Juan Reboredo & José Matías & Raquel Garcia-Rubio, 2012. "Nonlinearity in Forecasting of High-Frequency Stock Returns," Computational Economics, Springer;Society for Computational Economics, vol. 40(3), pages 245-264, October.
    18. Tsyplakov, Alexander, 2012. "Assessment of probabilistic forecasts: Proper scoring rules and moments," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 27(3), pages 115-132.
    19. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    20. Tsyplakov, Alexander, 2011. "Evaluating density forecasts: a comment," MPRA Paper 31184, University Library of Munich, Germany.
    21. Tao Hong & Katarzyna Maciejowska & Jakub Nowotarski & Rafal Weron, 2014. "Probabilistic load forecasting via Quantile Regression Averaging of independent expert forecasts," HSC Research Reports HSC/14/10, Hugo Steinhaus Center, Wroclaw University of Technology.
    22. Li, Yushu & Andersson, Jonas, 2014. "A Likelihood Ratio and Markov Chain Based Method to Evaluate Density Forecasting," Discussion Papers 2014/12, Norwegian School of Economics, Department of Business and Management Science.
    23. Lawrence, Michael & Goodwin, Paul & O'Connor, Marcus & Onkal, Dilek, 2006. "Judgmental forecasting: A review of progress over the last 25 years," International Journal of Forecasting, Elsevier, vol. 22(3), pages 493-518.
    24. Jakub Nowotarski & Rafal Weron, 2013. "Computing electricity spot price prediction intervals using quantile regression and forecast averaging," HSC Research Reports HSC/13/12, Hugo Steinhaus Center, Wroclaw University of Technology.
    25. Elena Ivona Dumitrescu & Christophe Hurlin & Jaouad Madkour, 2013. "Testing Interval Forecasts: a GMM-Based Approach," Post-Print hal-01385898, HAL.
    26. 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.
    27. Sayar Karmakar & Marek Chudy & Wei Biao Wu, 2020. "Long-term prediction intervals with many covariates," Papers 2012.08223, arXiv.org, revised Sep 2021.

  56. Michael P. Clements & Marianne Sensier, 2003. "Asymmetric output‐gap effects in Phillips Curve and mark‐up pricing models: Evidence for the US and the UK," Scottish Journal of Political Economy, Scottish Economic Society, vol. 50(4), pages 359-374, September.

    Cited by:

    1. Arnildo da Silva Correa & André Minella, 2006. "Nonlinear Mechanisms of the Exchange Rate Pass-Through: a Phillips curve model with threshold for Brazil," Working Papers Series 122, Central Bank of Brazil, Research Department.
    2. Ellington, Michael & Milas, Costas, 2019. "Global liquidity, money growth and UK inflation," Journal of Financial Stability, Elsevier, vol. 42(C), pages 67-74.
    3. Alessandra Dal Colle, 2011. "Finance–growth nexus: does causality withstand financial liberalization? Evidence from cointegrated VAR," Empirical Economics, Springer, vol. 41(1), pages 127-154, August.
    4. Bjørnstad, Roger & Kalstad, Kjartan Øren, 2010. "Increased price markup from union coordination: OECD panel evidence," Economics Discussion Papers 2010-13, Kiel Institute for the World Economy (IfW Kiel).
    5. Michael Arghyrou & Christopher Martin & Costas Milas, 2005. "Non-linear inflationary dynamics: evidence from the UK," Oxford Economic Papers, Oxford University Press, vol. 57(1), pages 51-69, January.
    6. Guglielmo Maria Caporale & Luis Alberiko Gil‐Alana & Tommaso Trani, 2022. "On the persistence of UK inflation: A long‐range dependence approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 439-454, January.
    7. Valadkhani, Abbas, 2014. "Switching impacts of the output gap on inflation: Evidence from Canada, the UK and the US," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 270-285.
    8. Abbas Valadkhani, 2015. "Asymmetric size-dependent effects of the output gap on inflation: US evidence from the last half a century," Applied Economics, Taylor & Francis Journals, vol. 47(33), pages 3525-3539, July.
    9. Denise Osborn & Marianne Sensier, 2007. "UK inflation: persistance, seasonality and monetary policy," Economics Discussion Paper Series 0716, Economics, The University of Manchester.
    10. D R Osborn & M Sensier, 2004. "Modelling UK Inflation: Persistence, Seasonality and Monetary Policy," Centre for Growth and Business Cycle Research Discussion Paper Series 46, Economics, The University of Manchester.
    11. Baharumshah, Ahmad Zubaidi & Soon, Siew-Voon & Wohar, Mark E., 2017. "Markov-switching analysis of exchange rate pass-through: Perspective from Asian countries," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 245-257.
    12. Christopher Martin & Michael Arghyrou & Costas Milas, 2004. "Nonlinear inflation dynamics: evidence from the UK," Money Macro and Finance (MMF) Research Group Conference 2003 59, Money Macro and Finance Research Group.
    13. Jennifer Castle & David Hendry, 2007. "Forecasting UK Inflation: the Roles of Structural Breaks and Time Disaggregation," Economics Series Working Papers 309, University of Oxford, Department of Economics.

  57. Dick van Dijk & Philip Hans Franses & Michael P. Clements & Jeremy Smith, 2003. "On SETAR non-linearity and forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(5), pages 359-375.
    See citations under working paper version above.
  58. Clements, Michael P., 2003. "Some possible directions for future research," International Journal of Forecasting, Elsevier, vol. 19(1), pages 1-3.

    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. Simionescu, Mihaela, 2014. "New Strategies to Improve the Accuracy of Predictions based on Monte Carlo and Bootstrap Simulations: An Application to Bulgarian and Romanian Inflation || Nuevas estrategias para mejorar la exactitud," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 18(1), pages 112-129, December.
    3. 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.
    4. Mihaela Simionescu, 2015. "A New Technique based on Simulations for Improving the Inflation Rate Forecasts in Romania," Working Papers of Institute for Economic Forecasting 150206, Institute for Economic Forecasting.
    5. Sanders, Dwight R. & Manfredo, Mark R., 2004. "Predicting Pork Supplies: An Application of Multiple Forecast Encompassing," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 36(3), pages 605-615, December.
    6. Hall, Stephen G. & Mitchell, James, 2007. "Combining density forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 1-13.

  59. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    See citations under working paper version above.
  60. Clements, Michael P. & Galvão, Ana Beatriz C., 2003. "Testing The Expectations Theory Of The Term Structure Of Interest Rates In Threshold Models," Macroeconomic Dynamics, Cambridge University Press, vol. 7(4), pages 567-585, September.

    Cited by:

    1. Frédérique Bec & Mélika Ben Salem, 2004. "L'ajustement à seuil des processus cointégrés. Que sait-on des modèles à trois régimes ?," Revue d'économie politique, Dalloz, vol. 114(4), pages 467-488.
    2. Araç, Ayşen & Yalta, A. Yasemin, 2015. "Testing the expectations hypothesis for the Eurozone: A nonlinear cointegration analysis," Finance Research Letters, Elsevier, vol. 15(C), pages 41-48.
    3. Katsuhiro Sugita, 2016. "Bayesian inference in Markov switching vector error correction model," Economics Bulletin, AccessEcon, vol. 36(3), pages 1534-1546.
    4. Alain W. HECQ, 2005. "Common Trends and Common Cycles in Latin America: A 2-step vs an Iterative Approach," Computing in Economics and Finance 2005 258, Society for Computational Economics.
    5. Peter Sephton & Janelle Mann, 2013. "Threshold Cointegration: Model Selection with an Application," Journal of Economics and Econometrics, Economics and Econometrics Society, vol. 56(2), pages 54-77.
    6. Zeno Rotondi, 2006. "The Macroeconomy and the Yield Curve: A Review of the Literature with Some New Evidence," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 65(2), pages 193-224, November.
    7. Daiki Maki, 2013. "Detecting cointegration relationships under nonlinear models: Monte Carlo analysis and some applications," Empirical Economics, Springer, vol. 45(1), pages 605-625, August.
    8. Ayşen ARAÇ, 2015. "Nonlinear Dynamics in Term Structure of Interest Rates: Evidence from the Euro Area," Sosyoekonomi Journal, Sosyoekonomi Society, issue 23(26).
    9. Maki, Daiki, 2015. "Wild bootstrap testing for cointegration in an ESTAR error correction model," Economic Modelling, Elsevier, vol. 47(C), pages 292-298.
    10. Galvão, Ana Beatriz C., 2003. "Multivariate Threshold Models: TVARs and TVECMs," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 23(1), May.
    11. Daiki Maki & Shin-ichi Kitasaka, 2015. "Residual-based tests for cointegration with three-regime TAR adjustment," Empirical Economics, Springer, vol. 48(3), pages 1013-1054, May.
    12. Bernard Njindan Iyke, 2017. "On the term structure of South African interest rates: cointegration and threshold adjustment," International Journal of Sustainable Economy, Inderscience Enterprises Ltd, vol. 9(4), pages 300-321.
    13. Daiki Maki, 2008. "The Performance of Variance Ratio Unit Root Tests Under Nonlinear Stationary TAR and STAR Processes: Evidence from Monte Carlo Simulations and Applications," Computational Economics, Springer;Society for Computational Economics, vol. 31(1), pages 77-94, February.
    14. Clements, Michael P. & Galvao, Ana Beatriz, 2004. "A comparison of tests of nonlinear cointegration with application to the predictability of US interest rates using the term structure," International Journal of Forecasting, Elsevier, vol. 20(2), pages 219-236.

  61. Clements, Michael P & Krolzig, Hans-Martin, 2003. "Business Cycle Asymmetries: Characterization and Testing Based on Markov-Switching Autoregressions," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 196-211, January.
    See citations under working paper version above.
  62. Clements, Michael P. & Smith, Jeremy, 2002. "Evaluating multivariate forecast densities: a comparison of two approaches," International Journal of Forecasting, Elsevier, vol. 18(3), pages 397-407.

    Cited by:

    1. Norman R. Swanson & Nii Ayi Armah, 2011. "Predictive Inference Under Model Misspecification with an Application to Assessing the Marginal Predictive Content of Money for Output," Departmental Working Papers 201103, Rutgers University, Department of Economics.
    2. Isao Ishida, 2005. "Scanning Multivariate Conditional Densities with Probability Integral Transforms," CARF F-Series CARF-F-045, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    3. Jonas Dovern & Hans Manner, 2018. "Order Invariant Tests for Proper Calibration of Multivariate Density Forecasts," CESifo Working Paper Series 7023, CESifo.
    4. Gloria Gonzalez‐Rivera & Yun Luo & Esther Ruiz, 2020. "Prediction regions for interval‐valued time series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(4), pages 373-390, June.
    5. 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.
    6. Dovern, Jonas & Manner, Hans, 2016. "Robust Evaluation of Multivariate Density Forecasts," VfS Annual Conference 2016 (Augsburg): Demographic Change 145547, Verein für Socialpolitik / German Economic Association.
    7. González-Rivera, Gloria & Luo, Yun & Ruiz Ortega, Esther, 2019. "Prediction regions for interval-valued time series," DES - Working Papers. Statistics and Econometrics. WS 29054, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Polanski, Arnold & Stoja, Evarist, 2012. "Efficient evaluation of multidimensional time-varying density forecasts, with applications to risk management," International Journal of Forecasting, Elsevier, vol. 28(2), pages 343-352.
    9. Knüppel, Malte & Krüger, Fabian & Pohle, Marc-Oliver, 2022. "Score-based calibration testing for multivariate forecast distributions," Discussion Papers 50/2022, Deutsche Bundesbank.
    10. Kheifets, Igor L., 2018. "Multivariate specification tests based on a dynamic Rosenblatt transform," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 1-14.
    11. Ruiz Ortega, Esther & Fresoli, Diego Eduardo & Pascual, Lorenzo, 2011. "Bootstrap forecast of multivariate VAR models without using the backward representation," DES - Working Papers. Statistics and Econometrics. WS ws113426, Universidad Carlos III de Madrid. Departamento de Estadística.
    12. Ko, Stanley I.M. & Park, Sung Y., 2013. "Multivariate density forecast evaluation: A modified approach," International Journal of Forecasting, Elsevier, vol. 29(3), pages 431-441.
    13. Dovern, Jonas & Manner, Hans, 2016. "Order Invariant Evaluation of Multivariate Density Forecasts," Working Papers 0608, University of Heidelberg, Department of Economics.
    14. 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.
    15. González-Rivera, Gloria & Yoldas, Emre, 2012. "Autocontour-based evaluation of multivariate predictive densities," International Journal of Forecasting, Elsevier, vol. 28(2), pages 328-342.
    16. Corradi, Valentina & Swanson, Norman R., 2004. "A test for the distributional comparison of simulated and historical data," Economics Letters, Elsevier, vol. 85(2), pages 185-193, November.
    17. Bhardwaj, Geetesh & Swanson, Norman R., 2006. "An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 539-578.
    18. Diep Duong & Norman Swanson, 2013. "Density and Conditional Distribution Based Specification Analysis," Departmental Working Papers 201312, Rutgers University, Department of Economics.
    19. Corradi, Valentina & Swanson, Norman R., 2006. "Bootstrap conditional distribution tests in the presence of dynamic misspecification," Journal of Econometrics, Elsevier, vol. 133(2), pages 779-806, August.
    20. Valentina Corradi & Norman Swanson, 2003. "The Block Bootstrap for Parameter Estimation Error In Recursive Estimation Schemes, With Applications to Predictive Evaluation," Departmental Working Papers 200313, Rutgers University, Department of Economics.
    21. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2008. "Partial Likelihood-Based Scoring Rules for Evaluating Density Forecasts in Tails," Discussion Papers 2008-10, School of Economics, The University of New South Wales.
    22. De Gooijer, Jan G. & Vidiella-i-Anguera, Antoni, 2004. "Forecasting threshold cointegrated systems," International Journal of Forecasting, Elsevier, vol. 20(2), pages 237-253.
    23. Shamiri, Ahmed & Shaari, Abu Hassan & Isa, Zaidi, 2007. "Practical Volatility Modeling for Financial Market Risk Management," MPRA Paper 9790, University Library of Munich, Germany, revised 15 May 2008.
    24. Corradi, Valentina & Swanson, Norman R., 2004. "Some recent developments in predictive accuracy testing with nested models and (generic) nonlinear alternatives," International Journal of Forecasting, Elsevier, vol. 20(2), pages 185-199.
    25. Cai, Lili & Swanson, Norman R., 2011. "In- and out-of-sample specification analysis of spot rate models: Further evidence for the period 1982-2008," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 743-764, September.
    26. Alexander, Carol & Lazar, Emese & Stanescu, Silvia, 2021. "Analytic moments for GJR-GARCH (1, 1) processes," International Journal of Forecasting, Elsevier, vol. 37(1), pages 105-124.
    27. Polanski, Arnold & Stoja, Evarist & Zhang, Ren, 2013. "Multidimensional risk and risk dependence," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 3286-3294.
    28. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
    29. 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.
    30. Norman Swanson & Oleg Korenok, 2006. "The Incremental Predictive Information Associated with Using Theoretical New Keynesian DSGE Models Versus Simple Linear Alternatives," Departmental Working Papers 200615, Rutgers University, Department of Economics.

  63. Ana B. C. Galvão & Michael P. Clements, 2002. "Conditional mean functions of non-linear models of US output," Empirical Economics, Springer, vol. 27(4), pages 569-586.

    Cited by:

    1. Beatriz C. Galvao, Ana, 2002. "Can non-linear time series models generate US business cycle asymmetric shape?," Economics Letters, Elsevier, vol. 77(2), pages 187-194, October.

  64. Michael P. Clements & David F. Hendry, 2002. "Modelling methodology and forecast failure," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 319-344, June.

    Cited by:

    1. Roberto Golinelli & Giuseppe Parigi, 2003. "What is this thing called confidence? A comparative analysis of consumer confidence indices in eight major countries," Temi di discussione (Economic working papers) 484, Bank of Italy, Economic Research and International Relations Area.
    2. Qin, Duo & He, Xinhua, 2012. "Modelling the impact of aggregate financial shocks external to the Chinese economy," BOFIT Discussion Papers 25/2012, Bank of Finland Institute for Emerging Economies (BOFIT).
    3. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    4. WAN, Shui-Ki & WANG, Shin-Huei & WOO, Chi-Keung, 2012. "Total tourist arrival forecast: aggregation vs. disaggregation," LIDAM Discussion Papers CORE 2012039, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. 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.
    6. 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.
    7. Salisu, Afees A. & Vo, Xuan Vinh, 2020. "Predicting stock returns in the presence of COVID-19 pandemic: The role of health news," International Review of Financial Analysis, Elsevier, vol. 71(C).
    8. Janine Aron & John Muellbauer, 2013. "New Methods for Forecasting Inflation, Applied to the US," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(5), pages 637-661, October.
    9. 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.
    10. David Hendry & Maozu Lu & Grayham E. Mizon, 2001. "Model Identification and Non-unique Structure," Economics Papers 2002-W10, Economics Group, Nuffield College, University of Oxford.
    11. Timmermann, Allan & Elliott, Graham, 2007. "Economic Forecasting," CEPR Discussion Papers 6158, C.E.P.R. Discussion Papers.
    12. Rishabh Choudhary & Chetan Ghate & Md Arbaj Meman, 2023. "Forecasting Core Inflation in India: A Four-Step Approach," IEG Working Papers 461, Institute of Economic Growth.
    13. 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.
    14. G. Solomon Osho, 2019. "A General Framework for Time Series Forecasting Model Using Autoregressive Integrated Moving Average-ARIMA and Transfer Functions," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 8(6), pages 1-23, November.
    15. Janine Aron & John Muellbauer & Rachel Sebudde, 2015. "Inflation forecasting models for Uganda: is mobile money relevant?," CSAE Working Paper Series 2015-17, Centre for the Study of African Economies, University of Oxford.
    16. 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.
    17. Janine Aron & John Muellbauer, 2008. "New methods for forecasting inflation and its sub-components: application to the USA," Economics Series Working Papers 406, University of Oxford, Department of Economics.
    18. Tannura, Michael A. & Irwin, Scott H. & Good, Darrel L., 2008. "Weather, Technology, and Corn and Soybean Yields in the U.S. Corn Belt," Marketing and Outlook Research Reports 37501, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics.
    19. Muellbauer, John & Aron, Janine, 2009. "Some Issues in Modeling and Forecasting Inflation in South Africa," CEPR Discussion Papers 7183, C.E.P.R. Discussion Papers.
    20. Aron, Janine & Muellbauer, John, 2012. "Improving forecasting in an emerging economy, South Africa: Changing trends, long run restrictions and disaggregation," International Journal of Forecasting, Elsevier, vol. 28(2), pages 456-476.
    21. Carrera, Cesar & Ledesma, Alan, 2015. "Proyección de la inflación agregada con modelos de vectores autorregresivos bayesianos," Working Papers 2015-003, Banco Central de Reserva del Perú.
    22. Nymoen, Ragnar, 2005. "Evaluating a Central Bank’s Recent Forecast Failure," Memorandum 22/2005, Oslo University, Department of Economics.
    23. Bardsen, Gunnar & Eitrheim, Oyvind & Jansen, Eilev S. & Nymoen, Ragnar, 2005. "The Econometrics of Macroeconomic Modelling," OUP Catalogue, Oxford University Press, number 9780199246502, Decembrie.
    24. Cesar Carrera & Alan Ledesma, 2015. "Aggregate Inflation Forecast with Bayesian Vector Autoregressive Models," Working Papers 50, Peruvian Economic Association.
    25. 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.
    26. Norman Swanson & Oleg Korenok, 2006. "The Incremental Predictive Information Associated with Using Theoretical New Keynesian DSGE Models Versus Simple Linear Alternatives," Departmental Working Papers 200615, Rutgers University, Department of Economics.
    27. Valentina Corradi & Norman Swanson, 2013. "Testing for Structural Stability of Factor Augmented Forecasting Models," Departmental Working Papers 201314, Rutgers University, Department of Economics.
    28. Dick van Dijk & Philip Hans Franses & Michael P. Clements & Jeremy Smith, 2003. "On SETAR non-linearity and forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(5), pages 359-375.
    29. Heather M Anderson & Farshid Vahid, 2010. "VARs, Cointegration and Common Cycle Restrictions," Monash Econometrics and Business Statistics Working Papers 14/10, Monash University, Department of Econometrics and Business Statistics.
    30. David Hendry & Michael P. Clements, 2010. "Forecasting from Mis-specified Models in the Presence of Unanticipated Location Shifts," Economics Series Working Papers 484, University of Oxford, Department of Economics.

  65. Clements, Michael P., 2002. "Comments on 'The state of macroeconomic forecasting'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 469-482, December.

    Cited by:

    1. Knut Are Aastveit & James Mitchell & Francesco Ravazzolo & Herman van Dijk, 2018. "The Evolution of Forecast Density Combinations in Economics," Tinbergen Institute Discussion Papers 18-069/III, Tinbergen Institute.
    2. Krkoska, Libor & Teksoz, Utku, 2007. "Accuracy of GDP growth forecasts for transition countries: Ten years of forecasting assessed," International Journal of Forecasting, Elsevier, vol. 23(1), pages 29-45.

  66. Hans-Martin Krolzig & Michael P. Clements, 2002. "Can oil shocks explain asymmetries in the US Business Cycle?," Empirical Economics, Springer, vol. 27(2), pages 185-204.

    Cited by:

    1. Melike E. Bildirici, 2020. "Environmental pollution, hydropower and nuclear energy generation before and after catastrophe: Bathtub‐Weibull curve and MS‐VECM methods," Natural Resources Forum, Blackwell Publishing, vol. 44(4), pages 289-310, November.
    2. Daniel, Betty C. & Hafner, Christian M. & Simar, Léopold & Manner, Hans, 2019. "Asymmetries In Business Cycles And The Role Of Oil Prices," Macroeconomic Dynamics, Cambridge University Press, vol. 23(4), pages 1622-1648, June.
    3. Mehmet Balcilar & Reneé van Eyden & Josine Uwilingiye & Rangan Gupta, 2014. "The impact of oil price on South African GDP growth: A Bayesian Markov Switching-VAR analysis," Working Papers 201470, University of Pretoria, Department of Economics.
    4. van Dijk, Dick & Hans Franses, Philip & Peter Boswijk, H., 2007. "Absorption of shocks in nonlinear autoregressive models," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4206-4226, May.
    5. 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.
    6. Troster, Victor & Shahbaz, Muhammad & Uddin, Gazi Salah, 2018. "Renewable Energy, Oil Prices, and Economic Activity: A Granger-causality in Quantiles Analysis," MPRA Paper 84194, University Library of Munich, Germany, revised 19 Jan 2018.
    7. Jamie L. Cross & Chenghan Hou & Bao H. Nguyen, 2018. "On the China factor in international oil markets: A regime switching approach," Working Papers No 11/2018, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    8. Engemann, Kristie M. & Kliesen, Kevin L. & Owyang, Michael T., 2011. "Do Oil Shocks Drive Business Cycles? Some U.S. And International Evidence," Macroeconomic Dynamics, Cambridge University Press, vol. 15(S3), pages 498-517, November.
    9. Ferreiro Javier Ojea, 2019. "Structural change in the link between oil and the European stock market: implications for risk management," Dependence Modeling, De Gruyter, vol. 7(1), pages 53-125, January.
    10. Cong, Rong-Gang & Shen, Shaochuan, 2013. "Relationships among Energy Price Shocks, Stock Market, and the Macroeconomy: Evidence from China," MPRA Paper 112211, University Library of Munich, Germany.
    11. Michael P. Clements & Hans-Martin Krolzig, 2004. "Can regime-switching models reproduce the business cycle features of US aggregate consumption, investment and output?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 9(1), pages 1-14.
    12. Michael P. Clements & David I. Harvey, 2010. "Forecast encompassing tests and probability forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(6), pages 1028-1062.
    13. Juan Carlos Cuestas & Paulo Jose Regis, 2008. "Nonlinearities and the order of integration of oil prices," NBS Discussion Papers in Economics 2008/15, Economics, Nottingham Business School, Nottingham Trent University.
    14. Melike Bildirici & Özgür Ömer Ersin, 2014. "Nonlinearity, Volatility and Fractional Integration in Daily Oil Prices: Smooth Transition Autoregressive ST-FI(AP)GARCH Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 108-135, October.
    15. Stan Radchenko & Oleg Korenok, 2004. "The role of permanent and transitory components in business cycle volatility moderation," Econometric Society 2004 North American Summer Meetings 149, Econometric Society.
    16. Raheem, Ibrahim & Olabisi, Nafisat, 2019. "What is new? The role of asymmetry and breaks in oil price–output growth volatility nexus," MPRA Paper 105361, University Library of Munich, Germany.
    17. 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.
    18. Giordani, P. & Kohn, R. & van Dijk, D.J.C., 2005. "A unified approach to nonlinearity, structural change and outliers," Econometric Institute Research Papers EI 2005-09, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    19. De-Chih Liu, 2010. "Monetary policy and the asymmetric job creation and destruction behaviour," Applied Economics Letters, Taylor & Francis Journals, vol. 17(8), pages 773-780.
    20. Straub, Roland & Barnett, Alina, 2008. "What drives U.S. current account fluctuations?," Working Paper Series 959, European Central Bank.
    21. Moradi, Alireza, 2016. "Modeling Business Cycle Fluctuations through Markov Switching VAR:An Application to Iran," MPRA Paper 73608, University Library of Munich, Germany.
    22. Aloui, Chaker & Jammazi, Rania, 2009. "The effects of crude oil shocks on stock market shifts behaviour: A regime switching approach," Energy Economics, Elsevier, vol. 31(5), pages 789-799, September.
    23. Nan Li & Simon S. Kwok, 2021. "Jointly determining the state dimension and lag order for Markov‐switching vector autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 471-491, July.
    24. Park, Sung Y. & Zhao, Guochang, 2010. "An estimation of U.S. gasoline demand: A smooth time-varying cointegration approach," Energy Economics, Elsevier, vol. 32(1), pages 110-120, January.
    25. Oleg Korenok & Bruce Mizrach, 2004. "The Microeconomics of Macroeconomic Asymmetries: Sectoral Driving Forces and Firm Level Characteristics," Computing in Economics and Finance 2004 266, Society for Computational Economics.
    26. Marcelo Savino Portugal & Igor Alexandre Clemente de Morais, 2004. "Business Cycle In The Industrial Production Of Brazilian States," Econometric Society 2004 Latin American Meetings 23, Econometric Society.
    27. Stanislav Radchenko, 2004. "Anticipated and unanticipated effects of crude oil prices and oil inventory changes on gasoline prices," Microeconomics 0406001, University Library of Munich, Germany.
    28. Vasif Abiyev & Reşat Ceylan & Munise Ilıkkan Özgür, 2015. "The Effects of Oil Price Shocks on Turkish Business Cycle: A Markov Switching Approach," International Journal of Business and Economic Sciences Applied Research (IJBESAR), International Hellenic University (IHU), Kavala Campus, Greece (formerly Eastern Macedonia and Thrace Institute of Technology - EMaTTech), vol. 8(2), pages 7-18, October.
    29. 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.
    30. Maria Dolores Gadea & Ana Gómez-Loscos & Antonio Montañes, 2011. "ECONOMIC GROWTH, INFLATION AND OIL SHOCKS: ARE THE 1970s COMING BACK?," Post-Print hal-00720578, HAL.
    31. Julien Chevallier, 2013. "Price relationships in crude oil futures: new evidence from CFTC disaggregated data," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 15(2), pages 133-170, April.
    32. Bildirici, Melike & Kayıkçı, Fazıl, 2022. "The relation between growth, energy imports, militarization and current account balance in China, Israel and South Korea," Energy, Elsevier, vol. 242(C).
    33. Ahmad R. Jalali‐Naini & Mehdi Asali, 2004. "Cyclical behaviour and shock‐persistence: crude oil prices," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 28(2), pages 107-131, June.
    34. Muellbauer, John & Nunziata, Luca, 2001. "Credit, the Stock Market and Oil: Forecasting US GDP," CEPR Discussion Papers 2906, C.E.P.R. Discussion Papers.
    35. Melike E. Bildirici & Sema Yılmaz Genç & Salih Boztuna, 2023. "Sustainability, Natural Gas Consumption, and Environmental Pollution in the Period of Industry 4.0 in Turkey: MS-Granger Causality and Fourier Granger Causality Analysis," Sustainability, MDPI, vol. 15(13), pages 1-14, July.
    36. 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).

  67. Clements, Michael P. & Smith, Jeremy, 2001. "Evaluating forecasts from SETAR models of exchange rates," Journal of International Money and Finance, Elsevier, vol. 20(1), pages 133-148, February.

    Cited by:

    1. Kurmas Akdogan, 2016. "Unemployment Hysteresis and Structural Change in Europe," Working Papers 1618, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    2. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    3. Laurent Ferrara & Dominique Guégan, 2006. "Detection of the Industrial Business Cycle using SETAR Models," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(3), pages 353-371.
    4. Ben R. Craig & Joachim G. Keller, 2003. "The empirical performance of option-based densities of foreign exchange," Working Papers (Old Series) 0313, Federal Reserve Bank of Cleveland.
    5. Sofiane Sekioua, 2004. "The forward unbiasedness hypothesis and the forward premium: a nonlinear analysis," Money Macro and Finance (MMF) Research Group Conference 2003 85, Money Macro and Finance Research Group.
    6. Shiyi Chen & Wolfgang K. Härdle & Kiho Jeong, 2010. "Forecasting volatility with support vector machine-based GARCH model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(4), pages 406-433.
    7. Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022. ""An application of deep learning for exchange rate forecasting"," IREA Working Papers 202201, University of Barcelona, Research Institute of Applied Economics, revised Jan 2022.
    8. G. Boero & E. Marrocu, 2002. "The performance of Setar Models: a regime conditional evaluation of point, interval and density forecasts," Working Paper CRENoS 200208, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    9. Man-Wai Ng & Wai-Sum Chan, 2004. "Robustness of alternative non-linearity tests for SETAR models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(3), pages 215-231.
    10. Manzan, Sebastiano & Zerom, Dawit, 2008. "A bootstrap-based non-parametric forecast density," International Journal of Forecasting, Elsevier, vol. 24(3), pages 535-550.
    11. 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.
    12. Rossen Anja, 2016. "On the Predictive Content of Nonlinear Transformations of Lagged Autoregression Residuals and Time Series Observations," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 236(3), pages 389-409, May.
    13. Dendramis, Yiannis & Kapetanios, George & Tzavalis, Elias, 2014. "Level shifts in stock returns driven by large shocks," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 41-51.
    14. Ferrara, L. & Marcellino, M. & Mogliani, M., 2012. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," Working papers 383, Banque de France.
    15. Baik, Hyeoncheol & Han, Sumin & Joo, Sunghoon & Lee, Kangbok, 2022. "A bank's optimal capital ratio: A time-varying parameter model to the partial adjustment framework," Journal of Banking & Finance, Elsevier, vol. 142(C).
    16. Ben R. Craig & Ernst Glatzer & Joachim G. Keller & Martin Scheicher, 2003. "The forecasting performance of German stock option densities," Working Papers (Old Series) 0312, Federal Reserve Bank of Cleveland.
    17. van Dijk, D.J.C. & Franses, Ph.H.B.F., 2003. "Selecting a Nonlinear Time Series Model using Weighted Tests of Equal Forecast Accuracy," Econometric Institute Research Papers EI 2003-10, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    18. Zacharias Psaradakis & Fabio Spagnolo, 2005. "Forecast performance of nonlinear error-correction models with multiple regimes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(2), pages 119-138.
    19. G. Boero & E. Marrocu, 2001. "Evaluating non-linear models on point and interval forecasts: an application with exchange rate returns," Working Paper CRENoS 200110, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    20. Reason Lesego Machete, 2011. "Early Warning with Calibrated and Sharper Probabilistic Forecasts," Papers 1112.6390, arXiv.org, revised Jan 2012.
    21. Yuzhi Cai, 2005. "A forecasting procedure for nonlinear autoregressive time series models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(5), pages 335-351.
    22. Shiyi Chen & Kiho Jeong & Wolfgang Härdle, 2008. "Support Vector Regression Based GARCH Model with Application to Forecasting Volatility of Financial Returns," SFB 649 Discussion Papers SFB649DP2008-014, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    23. Hyeyoen Kim, 2011. "Large Data Sets, Nonlinearity and the Speed of Adjustment to Real Exchange Rate Shocks," Post-Print hal-00665456, HAL.
    24. Luca Brugnolini & Antonello D’Agostino & Alex Tagliabracci, 2021. "Is Anything Predictable in Market-Based Surprises?," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 7(3), pages 387-410, November.
    25. Dimitrios I. Vortelinos & Konstantinos Gkillas, 2018. "Intraday realised volatility forecasting and announcements," International Journal of Banking, Accounting and Finance, Inderscience Enterprises Ltd, vol. 9(1), pages 88-118.
    26. Firat Melih Yilmaz & Ozer Arabaci, 2021. "Should Deep Learning Models be in High Demand, or Should They Simply be a Very Hot Topic? A Comprehensive Study for Exchange Rate Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 217-245, January.
    27. Md. Karimuzzaman & Nusrat Islam & Sabrina Afroz & Md. Moyazzem Hossain, 2021. "Predicting Stock Market Price of Bangladesh: A Comparative Study of Linear Classification Models," Annals of Data Science, Springer, vol. 8(1), pages 21-38, March.
    28. Gianna Boero & Emanuela Marrocu, 2005. "Evaluating non-linear models on point and interval forecasts: an application with exchange rates," Banca Nazionale del Lavoro Quarterly Review, Banca Nazionale del Lavoro, vol. 58(232), pages 91-120.
    29. Cai, Yuzhi, 2007. "A quantile approach to US GNP," Economic Modelling, Elsevier, vol. 24(6), pages 969-979, November.
    30. Akintunde & M.O & Kgosi & P.M. & Agunloye & O.K. & Olalude G. A., 2019. "Evaluating Forecast Performance of SETAR Model using Gross Domestic Product of Nigeria," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 8(4), pages 1-6.
    31. Ben R. Craig & Joachim G. Keller, 2004. "The forecast ability of risk-neutral densities of foreign exchange," Working Papers (Old Series) 0409, Federal Reserve Bank of Cleveland.
    32. Castle, Jennifer L. & Kurita, Takamitsu, 2021. "A dynamic econometric analysis of the dollar-pound exchange rate in an era of structural breaks and policy regime shifts," Journal of Economic Dynamics and Control, Elsevier, vol. 128(C).
    33. Miquel Clar & Juan-Carlos Duque & Rosina Moreno, 2007. "Forecasting business and consumer surveys indicators-a time-series models competition," Applied Economics, Taylor & Francis Journals, vol. 39(20), pages 2565-2580.
    34. Chan, Wai-Sum, 2022. "On temporal aggregation of some nonlinear time-series models," Econometrics and Statistics, Elsevier, vol. 21(C), pages 38-49.
    35. John Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Staff Working Papers 07-1, Bank of Canada.
    36. Yang, Jian & Su, Xiaojing & Kolari, James W., 2008. "Do Euro exchange rates follow a martingale? Some out-of-sample evidence," Journal of Banking & Finance, Elsevier, vol. 32(5), pages 729-740, May.

  68. Clements, Michael P & Taylor, Nick, 2001. "Robust Evaluation of Fixed-Event Forecast Rationality," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(4), pages 285-295, July.

    Cited by:

    1. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    2. Tian, Jing & Goodwin, Thomas, 2018. "An unobserved component modeling approach to evaluate multi-horizon forecasts," Working Papers 2018-04, University of Tasmania, Tasmanian School of Business and Economics.
    3. Chen, Qiwei & Costantini, Mauro & Deschamps, Bruno, 2016. "How accurate are professional forecasts in Asia? Evidence from ten countries," International Journal of Forecasting, Elsevier, vol. 32(1), pages 154-167.
    4. Goodwin, Thomas & Tian, Jing, 2017. "A state space approach to evaluate multi-horizon forecasts," Working Papers 2017-15, University of Tasmania, Tasmanian School of Business and Economics.
    5. Ullrich Heilemann & Karsten Müller, 2018. "Wenig Unterschiede – Zur Treffsicherheit Internationaler Prognosen und Prognostiker [Few differences—on the accuracy of international forecasts and forecaster]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 12(3), pages 195-233, December.
    6. Rosario Dell'Aquila & Elvezio Ronchetti, 2004. "Robust tests of predictive accuracy," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(2), pages 161-184.
    7. Lahiri, Kajal & Sheng, Xuguang, 2008. "Evolution of forecast disagreement in a Bayesian learning model," Journal of Econometrics, Elsevier, vol. 144(2), pages 325-340, June.

  69. Clements, Michael P. & Taylor, Nick, 2001. "Bootstrapping prediction intervals for autoregressive models," International Journal of Forecasting, Elsevier, vol. 17(2), pages 247-267.

    Cited by:

    1. Borbély, Dóra & Meier, Carsten-Patrick, 2003. "Macroeconomic interval forecasting: the case of assessing the risk of deflation in Germany," Kiel Working Papers 1153, Kiel Institute for the World Economy (IfW Kiel).
    2. João Henrique Gonçalves Mazzeu & Esther Ruiz & Helena Veiga, 2018. "Uncertainty And Density Forecasts Of Arma Models: Comparison Of Asymptotic, Bayesian, And Bootstrap Procedures," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 388-419, April.
    3. Schumacher, Christian, 2002. "Forecasting Trend Output in the Euro Area," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(8), pages 543-558, December.
    4. 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.
    5. Kim, Jae H. & Wong, Kevin & Athanasopoulos, George & Liu, Shen, 2011. "Beyond point forecasting: Evaluation of alternative prediction intervals for tourist arrivals," International Journal of Forecasting, Elsevier, vol. 27(3), pages 887-901, July.
    6. Merten, Michael & Rücker, Fabian & Schoeneberger, Ilka & Sauer, Dirk Uwe, 2020. "Automatic frequency restoration reserve market prediction: Methodology and comparison of various approaches," Applied Energy, Elsevier, vol. 268(C).
    7. Dag Kolsrud, 2008. "Stochastic Ceteris Paribus Simulations," Computational Economics, Springer;Society for Computational Economics, vol. 31(1), pages 21-43, February.
    8. Ahmed, Wajid Shakeel & Sheikh, Jibran & Ur-Rehman, Kashif & Shafi, khuram & Shad, Shafqat Ali & Butt, Faisal Shafique, 2020. "New continuum of stochastic static forecasting model for mutual funds at investment policy level," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
    9. Alonso Fernández, Andrés Modesto & Bastos, Guadalupe & García-Martos, Carolina, 2017. "BIAS correction for dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS 24029, Universidad Carlos III de Madrid. Departamento de Estadística.
    10. Sylvain Robbiano & Matthieu Saumard & Michel Curé, 2016. "Improving prediction performance of stellar parameters using functional models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(8), pages 1465-1476, June.
    11. Reeves, Jonathan J., 2005. "Bootstrap prediction intervals for ARCH models," International Journal of Forecasting, Elsevier, vol. 21(2), pages 237-248.
    12. 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.
    13. Galvao, Ana Beatriz & Costa, Sonia, 2013. "Does the euro area forward rate provide accurate forecasts of the short rate?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 131-141.
    14. Michael Wolf & Dan Wunderli, 2012. "Bootstrap joint prediction regions," ECON - Working Papers 064, Department of Economics - University of Zurich, revised May 2013.
    15. Helmut Luetkepohl, 2009. "Forecasting Aggregated Time Series Variables: A Survey," Economics Working Papers ECO2009/17, European University Institute.
    16. Jae H. Kim, 2004. "Bias-corrected bootstrap prediction regions for vector autoregression," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(2), pages 141-154.
    17. Anna Staszewska-Bystrova, 2009. "Bootstrap Confidence Bands for Forecast Paths," Working Papers 024, COMISEF.
    18. 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.
    19. Li, Jing, 2011. "Bootstrap prediction intervals for SETAR models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 320-332, April.
    20. Valentina Corradi & Norman R. Swanson, 2003. "A Test for Comparing Multiple Misspecified Conditional Distributions," Departmental Working Papers 200314, Rutgers University, Department of Economics.
    21. Kim, Jae H., 2004. "Bootstrap prediction intervals for autoregression using asymptotically mean-unbiased estimators," International Journal of Forecasting, Elsevier, vol. 20(1), pages 85-97.
    22. Dag Kolsrud, 2007. "Time-simultaneous prediction band for a time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(3), pages 171-188.
    23. Carriero, Andrea & Galvão, Ana Beatriz & Kapetanios, George, 2019. "A comprehensive evaluation of macroeconomic forecasting methods," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1226-1239.
    24. Galvao, Ana Beatriz & Garratt, Anthony & Mitchell, James, 2020. "Does Judgment Improve Macroeconomic Density Forecasts?," EMF Research Papers 33, Economic Modelling and Forecasting Group.
    25. Marcellino, Massimiliano & Knüppel, Malte & Jordà , Òscar, 2010. "Empirical Simultaneous Confidence Regions for Path-Forecasts," CEPR Discussion Papers 7797, C.E.P.R. Discussion Papers.
    26. Felix Wick & Ulrich Kerzel & Martin Hahn & Moritz Wolf & Trapti Singhal & Daniel Stemmer & Jakob Ernst & Michael Feindt, 2021. "Demand Forecasting of Individual Probability Density Functions with Machine Learning," SN Operations Research Forum, Springer, vol. 2(3), pages 1-39, September.
    27. Chan, W.S & Cheung, S.H & Wu, K.H, 2004. "Multiple forecasts with autoregressive time series models: case studies," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(3), pages 421-430.
    28. 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.
    29. Benner, Joachim & Borbély, Dóra & Boss, Alfred & Kamps, Annette & Meier, Carsten-Patrick & Oskamp, Frank & Scheide, Joachim & Schmidt, Rainer, 2003. "Deutschland: Stagnation hält vorerst an," Open Access Publications from Kiel Institute for the World Economy 2984, Kiel Institute for the World Economy (IfW Kiel).
    30. Knut Are Aastveit & Claudia Foroni & Francesco Ravazzolo, 2014. "Density forecasts with MIDAS models," Working Paper 2014/10, Norges Bank.
    31. Meier, Carsten-Patrick, 2004. "Investigating the impact of an appreciation of the euro in a small macroeconometric model of Germany and the euro area," Kiel Working Papers 1204, Kiel Institute for the World Economy (IfW Kiel).
    32. Knüppel, Malte, 2014. "Forecast-error-based estimation of forecast uncertainty when the horizon is increased," Discussion Papers 40/2014, Deutsche Bundesbank.
    33. 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.
    34. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.
    35. 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.
    36. 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.
    37. Dick van Dijk & Philip Hans Franses & Michael P. Clements & Jeremy Smith, 2003. "On SETAR non-linearity and forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(5), pages 359-375.
    38. Clements, Michael P. & Kim, Jae H., 2007. "Bootstrap prediction intervals for autoregressive time series," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3580-3594, April.
    39. Luisa Bisaglia & Margherita Gerolimetto, 2019. "Model-based INAR bootstrap for forecasting INAR(p) models," Computational Statistics, Springer, vol. 34(4), pages 1815-1848, December.
    40. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    41. Federico Bassetti & Roberto Casarin & Francesco Ravazzolo, 2019. "Density Forecasting," BEMPS - Bozen Economics & Management Paper Series BEMPS59, Faculty of Economics and Management at the Free University of Bozen.
    42. Anna Staszewska-Bystrova & Peter Winker, 2016. "Improved bootstrap prediction intervals for SETAR models," Statistical Papers, Springer, vol. 57(1), pages 89-98, March.

  70. Clements, Michael P. & Hendry, David F., 2001. "An Historical Perspective on Forecast Errors," National Institute Economic Review, National Institute of Economic and Social Research, vol. 177, pages 100-112, July.

    Cited by:

    1. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    2. David Hendry & Grayham E. Mizon, 2012. "Forecasting from Structural Econometric Models," Economics Series Working Papers 597, University of Oxford, Department of Economics.
    3. 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.
    4. David Hendry & Grayham E. Mizon, 2001. "Forecasting in the Presence of Structural Breaks and Policy Regime Shifts," Economics Papers 2002-W12, Economics Group, Nuffield College, University of Oxford.

  71. Michael P. Clements & David F.Hendry, 2001. "Forecasting with difference-stationary and trend-stationary models," Econometrics Journal, Royal Economic Society, vol. 4(1), pages 1-19.
    See citations under working paper version above.
  72. Michael P. Clements & David F. Hendry, 1999. "On winning forecasting competitions in economics," Spanish Economic Review, Springer;Spanish Economic Association, vol. 1(2), pages 123-160.

    Cited by:

    1. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    2. David F. Hendry, 2002. "Forecast Failure, Expectations Formation and the Lucas Critique," Annals of Economics and Statistics, GENES, issue 67-68, pages 21-40.
    3. Corradi, Valentina & Swanson, Norman R. & Olivetti, Claudia, 2001. "Predictive ability with cointegrated variables," Journal of Econometrics, Elsevier, vol. 104(2), pages 315-358, September.
    4. Chien-Chung Nieh & Hwey-Yun Yau & Ken Hung & Hong-Kou Ou & Shine Hung, 2013. "Cointegration and causal relationships among steel prices of Mainland China, Taiwan, and USA in the presence of multiple structural changes," Empirical Economics, Springer, vol. 44(2), pages 545-561, April.
    5. Theologos Pantelidis & Nikitas Pittis, 2009. "Estimation and forecasting in first-order vector autoregressions with near to unit roots and conditional heteroscedasticity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 612-630.
    6. Francis X. Diebold & Lutz Kilian, 1999. "Unit Root Tests Are Useful for Selecting Forecasting Models," NBER Working Papers 6928, National Bureau of Economic Research, Inc.
    7. Corradi, V. & Swanson, N.R., 2000. "A Consistent Test for Nonlinear Out of Sample Predictive Accuracy," Discussion Papers 0012, University of Exeter, Department of Economics.
    8. Valentina Corradi & Norman R. Swanson, 2003. "The Effect of Data Transformation on Common Cycle, Cointegration and Unit Root Tests: Monte Carlo Results and a Simple Test," Departmental Working Papers 200322, Rutgers University, Department of Economics.
    9. Simon C. Smith, 2020. "Equity premium prediction and structural breaks," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 25(3), pages 412-429, July.
    10. 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.
    11. David Hendry, 2000. "A General Forecast-error Taxonomy," Econometric Society World Congress 2000 Contributed Papers 0608, Econometric Society.
    12. T. Thanh-Binh Nguyen & Kuan-Min Wang, 2010. "Causality between housing returns, inflation and economic growth with endogenous breaks," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 8(1), pages 95-115.
    13. Corradi, Valentina & Swanson, Norman R., 2004. "Some recent developments in predictive accuracy testing with nested models and (generic) nonlinear alternatives," International Journal of Forecasting, Elsevier, vol. 20(2), pages 185-199.
    14. Hendry, David F., 2000. "On detectable and non-detectable structural change," Structural Change and Economic Dynamics, Elsevier, vol. 11(1-2), pages 45-65, July.
    15. Hyein Shim & Hyeyoen Kim & Sunghyun Kim & Doojin Ryu, 2016. "Testing the relative purchasing power parity hypothesis: the case of Korea," Applied Economics, Taylor & Francis Journals, vol. 48(25), pages 2383-2395, May.
    16. Dungey, Mardi & Fry, Renee, 2000. "A Multi-Country Structural VAR Model," Departmental Working Papers 2001-04, The Australian National University, Arndt-Corden Department of Economics.
    17. Frugier, Alain, 2016. "Returns, volatility and investor sentiment: Evidence from European stock markets," Research in International Business and Finance, Elsevier, vol. 38(C), pages 45-55.

  73. Michael P. Clements & Reinhard Madlener, 1999. "Seasonality, Cointegration, and Forecasting UK Residential Energy Demand," Scottish Journal of Political Economy, Scottish Economic Society, vol. 46(2), pages 185-206, May.

    Cited by:

    1. Zachariadis, Theodoros & Hadjinicolaou, Panos, 2014. "The effect of climate change on electricity needs – A case study from Mediterranean Europe," Energy, Elsevier, vol. 76(C), pages 899-910.
    2. Hunt, Lester C. & Judge, Guy & Ninomiya, Yasushi, 2003. "Underlying trends and seasonality in UK energy demand: a sectoral analysis," Energy Economics, Elsevier, vol. 25(1), pages 93-118, January.
    3. Pinjari, Abdul Rawoof & Bhat, Chandra, 2021. "Computationally efficient forecasting procedures for Kuhn-Tucker consumer demand model systems: Application to residential energy consumption analysis," Journal of choice modelling, Elsevier, vol. 39(C).
    4. Zachariadis, Theodoros & Pashourtidou, Nicoletta, 2007. "An empirical analysis of electricity consumption in Cyprus," Energy Economics, Elsevier, vol. 29(2), pages 183-198, March.
    5. Zachariadis, Theodoros, 2010. "Forecast of electricity consumption in Cyprus up to the year 2030: The potential impact of climate change," Energy Policy, Elsevier, vol. 38(2), pages 744-750, February.
    6. Hondroyiannis, George, 2004. "Estimating residential demand for electricity in Greece," Energy Economics, Elsevier, vol. 26(3), pages 319-334, May.
    7. Zachariadis, Theodoros & Poullikkas, Andreas, 2012. "The costs of power outages: A case study from Cyprus," Energy Policy, Elsevier, vol. 51(C), pages 630-641.
    8. Herrerias, M.J., 2013. "Seasonal anomalies in electricity intensity across Chinese regions," Applied Energy, Elsevier, vol. 112(C), pages 1548-1557.
    9. Bhattacharyya, Subhes C. & Timilsina, Govinda R., 2010. "Modelling energy demand of developing countries: Are the specific features adequately captured?," Energy Policy, Elsevier, vol. 38(4), pages 1979-1990, April.
    10. Mudassir Zaman & Farzana Shaheen & Azad Haider & Sadia Qamar, 2015. "Examining Relationship between Electricity Consumption and its Major Determinants in Pakistan," International Journal of Energy Economics and Policy, Econjournals, vol. 5(4), pages 998-1009.
    11. M.Adetunji BABATUNDE & M.Isa SHAUIBU, 2011. "The Demand for Residential Electricity in Nigeria," Pakistan Journal of Applied Economics, Applied Economics Research Centre, vol. 21, pages 1-13.
    12. Noel Alter & Shabib Haider Syed, 2011. "An Empirical Analysis of Electricity Demand in Pakistan," International Journal of Energy Economics and Policy, Econjournals, vol. 1(4), pages 116-139.
    13. Narayan, Paresh Kumar & Smyth, Russell & Prasad, Arti, 2007. "Electricity consumption in G7 countries: A panel cointegration analysis of residential demand elasticities," Energy Policy, Elsevier, vol. 35(9), pages 4485-4494, September.
    14. Lester C. Hunt & Guy Judge & Yashushi Ninomiya, 2000. "Modelling Technical Progress: An Application of the Stochastic Trend Model to UK Energy Demand," Surrey Energy Economics Centre (SEEC), School of Economics Discussion Papers (SEEDS) 99, Surrey Energy Economics Centre (SEEC), School of Economics, University of Surrey.
    15. Maria J. Herrerias & Eric Girardin, 2013. "Seasonal Patterns of Energy in China," Post-Print hal-01499617, HAL.
    16. Inchauspe, Julian & Li, Jun & Park, Jason, 2020. "Seasonal patterns of global oil consumption: Implications for long term energy policy," Journal of Policy Modeling, Elsevier, vol. 42(3), pages 536-556.
    17. Jamil, Faisal & Ahmad, Eatzaz, 2011. "Income and price elasticities of electricity demand: Aggregate and sector-wise analyses," Energy Policy, Elsevier, vol. 39(9), pages 5519-5527, September.

  74. Clements, Michael P & Smith, Jeremy, 1999. "A Monte Carlo Study of the Forecasting Performance of Empirical SETAR Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(2), pages 123-141, March-Apr.
    See citations under working paper version above.
  75. Clements, Michael P. & Hendry, David F., 1998. "Forecasting economic processes," International Journal of Forecasting, Elsevier, vol. 14(1), pages 111-131, March.

    Cited by:

    1. Fildes, Robert & Stekler, Herman, 2002. "The state of macroeconomic forecasting," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 435-468, December.
    2. Carlos Capistrán-Carmona, 2005. "Bias in Federal Reserve Inflation Forecasts: Is the Federal Reserve Irrational or Just Cautious?," Computing in Economics and Finance 2005 127, Society for Computational Economics.
    3. David E. Bloom & David Canning & Günther Fink & Jocelyn E. Finlay, 2007. "Does Age Structure Forecast Economic Growth?," NBER Working Papers 13221, National Bureau of Economic Research, Inc.
    4. Pesaran, M.H. & Pettenuzzo, D. & Timmermann, A., 2006. "Learning, Structural Instability and Present Value Calculations," Cambridge Working Papers in Economics 0602, Faculty of Economics, University of Cambridge.
    5. David Hendry & Guillaume Chevillon, 2004. "Non-Parametric Direct Multi-step Estimation for Forecasting Economic Processes," Economics Series Working Papers 196, University of Oxford, Department of Economics.
    6. Kunst, Robert M. & Jumah, Adusei, 2004. "Toward a Theory of Evaluating Predictive Accuracy," Economics Series 162, Institute for Advanced Studies.
    7. Malmberg, Bo & Lindh, Thomas, 2004. "Demographically based global income forecasts up to the year 2050," Arbetsrapport 2004:7, Institute for Futures Studies.
    8. Guillaume Chevillon, 2007. "Direct Multi‐Step Estimation And Forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 746-785, September.
    9. Hendry, David F., 2006. "Robustifying forecasts from equilibrium-correction systems," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 399-426.
    10. Filippo di Mauro & L. Vanessa Smith & Stephane Dees & M. Hashem Pesaran, 2007. "Exploring the international linkages of the euro area: a global VAR analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 1-38.
    11. Pesaran, M. Hashem & Timmermann, Allan, 2004. "How costly is it to ignore breaks when forecasting the direction of a time series?," International Journal of Forecasting, Elsevier, vol. 20(3), pages 411-425.
    12. Luca Nocciola, "undated". "Finite sample forecast properties and window length under breaks in cointegrated systems," Discussion Papers 19/07, University of Nottingham, Granger Centre for Time Series Econometrics.
    13. 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.
    14. Book Review, 2000. "Book review," International Journal of Forecasting, Elsevier, vol. 16(1), pages 132-133.
    15. 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.
    16. Costas Anyfantakis & Guglielmo Maria Caporale & Nikitas Pittis, 2008. "Parameter instability and forecasting performance: a Monte Carlo study," International Journal of Business Forecasting and Marketing Intelligence, Inderscience Enterprises Ltd, vol. 1(1), pages 1-20.
    17. Lorena Skufi & Adam Gersl, 2022. "Using Macro-Financial Models to Simulate Macroeconomic Developments During the Covid-19 Pandemic: The Case of Albania," Working Papers IES 2022/24, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Sep 2022.
    18. Wani, M.H. & Paul, Ranjit Kumar & Bazaz, Naseer H. & Manzoor, M., 2015. "Market integration and Price Forecasting of Apple in India," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 70(2), pages 1-13.
    19. Pesaran, M. Hashem & Pettenuzzo, Davide & Timmermann, Allan, 2004. "Forecasting Time Series Subject to Multiple Structural Breaks," IZA Discussion Papers 1196, Institute of Labor Economics (IZA).
    20. Clements, M.P. & Smith, J., 1997. "Forecasting Seasonal UK Consumption Components," The Warwick Economics Research Paper Series (TWERPS) 487, University of Warwick, Department of Economics.
    21. Ming-Chih Lee & Chien-Liang Chiu & Wan-Hsiu Cheng, 2007. "Enhancing Forecast Accuracy By Using Long Estimation Periods," The International Journal of Business and Finance Research, The Institute for Business and Finance Research, vol. 1(2), pages 1-9.
    22. Alessandro Casini, 2018. "Tests for Forecast Instability and Forecast Failure under a Continuous Record Asymptotic Framework," Papers 1803.10883, arXiv.org, revised Dec 2018.
    23. Li, Gang & Li, Yong, 2015. "Forecasting copper futures volatility under model uncertainty," Resources Policy, Elsevier, vol. 46(P2), pages 167-176.
    24. Crespo-Cuaresma, Jesus, 2000. "Forecasting European GDP Using Self-Exciting Threshold Autoregressive Models. A Warning," Economics Series 79, Institute for Advanced Studies.
    25. Wang, Jianzhou & Zhu, Suling & Zhang, Wenyu & Lu, Haiyan, 2010. "Combined modeling for electric load forecasting with adaptive particle swarm optimization," Energy, Elsevier, vol. 35(4), pages 1671-1678.
    26. Raphael Markellos & Terence Mills, 2003. "Asset pricing dynamics," The European Journal of Finance, Taylor & Francis Journals, vol. 9(6), pages 533-556.
    27. Ricardo Mestre & Peter McAdam, 2011. "Is forecasting with large models informative? Assessing the role of judgement in macroeconomic forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(3), pages 303-324, April.
    28. Fokin, Nikita, 2021. "The importance of modeling structural breaks in forecasting Russian GDP," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 63, pages 5-29.
    29. Junttila, Juha, 2001. "Structural breaks, ARIMA model and Finnish inflation forecasts," International Journal of Forecasting, Elsevier, vol. 17(2), pages 203-230.
    30. Georgios Kouretas & Eleni Constantinou & Robert Georgiades & Avo Kazandjian, 2005. "Regime Switching and Artificial Neural Network Forecasting of the Cyprus Stock Exchange Daily Returns," Money Macro and Finance (MMF) Research Group Conference 2005 46, Money Macro and Finance Research Group.
    31. Batchelor, Roy & Alizadeh, Amir & Visvikis, Ilias, 2007. "Forecasting spot and forward prices in the international freight market," International Journal of Forecasting, Elsevier, vol. 23(1), pages 101-114.
    32. Elliott, Graham & Timmermann, Allan G, 2016. "Forecasting in Economics and Finance," University of California at San Diego, Economics Working Paper Series qt6z55v472, Department of Economics, UC San Diego.
    33. 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.
    34. Marc Brisson & Bryan Campbell & John W. Galbraith, 2001. "Forecasting Some Low-Predictability Time Series Using Diffusion Indices," CIRANO Working Papers 2001s-46, CIRANO.
    35. Rangan Gupta & Marius Jurgilas & Alain Kabundi & Stephen M. Miller, 2009. "Monetary Policy and Housing Sector Dynamics in a Large-Scale Bayesian Vector Autoregressive Model," Working papers 2009-19, University of Connecticut, Department of Economics.
    36. Allan Timmermann & Andrew J. Patton, 2004. "Properties of Optimal Forecasts," Econometric Society 2004 North American Winter Meetings 234, Econometric Society.
    37. Kraay, Aart & Monokroussos, George, 1999. "Growth forecasts using time series and growth models," Policy Research Working Paper Series 2224, The World Bank.
    38. Victor Bystrov, 2006. "Forecasting Emerging Market Indicators: Brazil and Russia," Economics Working Papers ECO2006/12, European University Institute.
    39. Maghyereh, Aktham, 2003. "Financial Liberalization and Stability Demand for Money in Emerging Economies: Evidence from Jordan," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 3(2).
    40. Rangan Gupta & Stephen M. Miller & Dylan van Wyk, 2010. "Financial Market Liberalization, Monetary Policy, and Housing Price Dynamics," Working papers 2010-06, University of Connecticut, Department of Economics.
    41. Vasco J. Gabriel & Luis F. Martins, 2000. "The Forecast Performance of Long Memory and Markov Switching Models," NIPE Working Papers 2/2000, NIPE - Universidade do Minho.
    42. Eleni Constantinou & Robert Georgiades & Avo Kazandjian & George Kouretas, 2005. "Regime Switching and Artificial Neural Network Forecasting," Working Papers 0502, University of Crete, Department of Economics.
    43. Li, Yong & Huang, Wei-Ping & Zhang, Jie, 2013. "Forecasting volatility in the Chinese stock market under model uncertainty," Economic Modelling, Elsevier, vol. 35(C), pages 231-234.

  76. Michael P. Clements & Hans-Martin Krolzig, 1998. "A comparison of the forecast performance of Markov-switching and threshold autoregressive models of US GNP," Econometrics Journal, Royal Economic Society, vol. 1(Conferenc), pages 47-75.
    See citations under working paper version above.
  77. Clements, Michael P. & Smith, Jeremy, 1997. "The performance of alternative forecasting methods for SETAR models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 463-475, December.
    See citations under working paper version above.
  78. Clements, Michael P. & Hendry, David F., 1997. "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, Elsevier, vol. 13(3), pages 341-355, September.

    Cited by:

    1. Guorui Bian & Michael McAleer & Wing-Keung Wong, 2013. "Robust Estimation and Forecasting of the Capital Asset Pricing Model," Tinbergen Institute Discussion Papers 13-036/III, Tinbergen Institute.
    2. David Hendry & Andrew B. Martinez, 2016. "Evaluating Multi-Step System Forecasts with Relatively Few Forecast-Error Observations," Economics Series Working Papers 784, University of Oxford, Department of Economics.
    3. A. Asrat Atsedeweyn & K. Srinivasa Rao, 2014. "Linear regression model with new symmetric distributed errors," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(2), pages 364-381, February.
    4. Bharat Barot & Zan Yang, 2004. "House Prices and Housing Investment in Sweden and the UK. Econometric analysis for the period 1970-1998," Macroeconomics 0409022, University Library of Munich, Germany.
    5. Anna Serena Vergori, 2012. "Forecasting Tourism Demand: The Role of Seasonality," Tourism Economics, , vol. 18(5), pages 915-930, October.
    6. Bharat Barot, 2004. "Growth and Business Cycles for the Swedish Economy 1963-1999," Macroeconomics 0409017, University Library of Munich, Germany.
    7. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    8. Gustavsson, Patrik & Nordström, Jonas, 1999. "The Impact of Seasonal Unit Roots and Vector ARMA Modeling on Forecasting Monthly Tourism Flows," Working Paper Series 150, Trade Union Institute for Economic Research, revised 01 Jul 2000.
    9. Proietti, Tommaso & Lütkepohl, Helmut, 2013. "Does the Box–Cox transformation help in forecasting macroeconomic time series?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 88-99.
    10. Elena Ganón & Ina Tiscordio, 2007. "Un análisis de variables fiscales del Gobierno Central del Uruguay para el período 1989-2006," Documentos de trabajo 2007005, Banco Central del Uruguay.
    11. Bharat Barot, 2005. "How Accurate Are The Swedish Forecasters On Gdp-Growth,Cpi- Inflation And Unemployment? (1993-2001)," Macroeconomics 0510017, University Library of Munich, Germany.
    12. Franses, Ph.H.B.F. & van Dijk, D.J.C., 2001. "The forecasting performance of various models for seasonality and nonlinearity for quarterly industrial production," Econometric Institute Research Papers EI 2001-14, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    13. Löf, M. & Franses, Ph.H.B.F., 2000. "On forecasting cointegrated seasonal time series," Econometric Institute Research Papers EI 2000-04/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    14. Mårten Löf & Johan Lyhagen, 2003. "On seasonal error correction when the processes include different numbers of unit roots," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(5), pages 377-389.
    15. Pami Dua & Lokendra Kumawat, 2005. "Modelling and Forecasting Seasonality in Indian Macroeconomic Time Series," Working papers 136, Centre for Development Economics, Delhi School of Economics.
    16. Osborn, Denise R. & Heravi, Saeed & Birchenhall, C. R., 1999. "Seasonal unit roots and forecasts of two-digit European industrial production," International Journal of Forecasting, Elsevier, vol. 15(1), pages 27-47, February.
    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. Paulo M.M. Rodrigues & Pedro M.D.C.B. Gouveia, 2004. "An Application of PAR Models for Tourism Forecasting," Tourism Economics, , vol. 10(3), pages 281-303, September.
    19. Carmine Pappalardo & Gianfranco Piras, 2004. "Vector-Autoregression Approach to Forecast Italian Imports," ISAE Working Papers 42, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
    20. Clements, M.P. & Smith, J., 1997. "Forecasting Seasonal UK Consumption Components," The Warwick Economics Research Paper Series (TWERPS) 487, University of Warwick, Department of Economics.
    21. Ericsson Neil R., 2008. "Comment on "Economic Forecasting in a Changing World" (by Michael Clements and David Hendry)," Capitalism and Society, De Gruyter, vol. 3(2), pages 1-18, October.
    22. Harri, Ardian & Muhammad, Andrew & Anderson, John D., 2008. "Estimating a Demand System with Seasonally Differenced Data," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6427, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    23. Sujata Kar, 2010. "A Periodic Autoregressive Model of Indian WPI Inflation," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 4(3), pages 279-292, August.
    24. Barot, Bharat & Yang, Zan, 2002. "House Prices and Housing Investment in Sweden and the United Kingdom: Econometric Analysis for the Period 1970-1998," Working Papers 80, National Institute of Economic Research.
    25. Dillon Alleyne, 2006. "Can Seasonal Unit Root Testing Improve the Forecasting Accuracy of Tourist Arrivals?," Tourism Economics, , vol. 12(1), pages 45-64, March.
    26. Andrew Martinez, 2017. "Testing for Differences in Path Forecast Accuracy: Forecast-Error Dynamics Matter," Working Papers (Old Series) 1717, Federal Reserve Bank of Cleveland.
    27. Luetkepohl Helmut & Xu Fang, 2011. "Forecasting Annual Inflation with Seasonal Monthly Data: Using Levels versus Logs of the Underlying Price Index," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-23, February.
    28. John D. Levendis, 2018. "Time Series Econometrics," Springer Texts in Business and Economics, Springer, number 978-3-319-98282-3, December.
    29. Wing-Keung Wong & Guorui Bian, 2005. "Robust Estimation of Multiple Regression Model with Non-normal Error: Symmetric Distribution," Monash Economics Working Papers 09/05, Monash University, Department of Economics.
    30. Jan G. de Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Tinbergen Institute Discussion Papers 05-068/4, Tinbergen Institute.

  79. Clements, Michael P & Hendry, David F, 1996. "Multi-step Estimation for Forecasting," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 58(4), pages 657-684, November.
    See citations under working paper version above.
  80. Clements, Michael P & Hendry, David F, 1996. "Intercept Corrections and Structural Change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 475-494, Sept.-Oct.

    Cited by:

    1. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    2. 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.
    3. Jari Hännikäinen, 2014. "Multi-step forecasting in the presence of breaks," Working Papers 1494, Tampere University, Faculty of Management and Business, Economics.
    4. Ericsson, Neil R., 2017. "Economic forecasting in theory and practice: An interview with David F. Hendry," International Journal of Forecasting, Elsevier, vol. 33(2), pages 523-542.
    5. Niu, Linlin & Xu, Xiu & Chen, Ying, 2015. "An adaptive approach to forecasting three key macroeconomic variables for transitional China," BOFIT Discussion Papers 12/2015, Bank of Finland Institute for Emerging Economies (BOFIT).
    6. Moosa, Imad A. & Vaz, John J., 2016. "Cointegration, error correction and exchange rate forecasting," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 44(C), pages 21-34.
    7. Massimiliano Marcellino, "undated". "Further Results on MSFE Encompassing," Working Papers 143, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    8. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    9. Gustavsson, Patrik & Nordström, Jonas, 1999. "The Impact of Seasonal Unit Roots and Vector ARMA Modeling on Forecasting Monthly Tourism Flows," Working Paper Series 150, Trade Union Institute for Economic Research, revised 01 Jul 2000.
    10. Barakchian , Seyed Mahdi, 2012. "Implications of Cointegration for Forecasting: A Review and an Empirical Analysis," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 7(1), pages 87-118, October.
    11. Jorge Luis Tonetto & Adelar Fochezatto & Josep Miquel Pique, 2023. "The Impact of the COVID-19 Pandemic on the Use of the Menor Preço Brasil Application," Administrative Sciences, MDPI, vol. 13(11), pages 1-19, October.
    12. 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.
    13. Markus Baltzer & Gerhard Kling, 2005. "Predictability of future economic growth and the credibility of different monetary regimes in Germany, 1870 - 2003," Working Papers 5023, Economic History Society.
    14. Yoga Sasmita & Heri Kuswanto & Dedy Dwi Prastyo, 2024. "State-Dependent Model Based on Singular Spectrum Analysis Vector for Modeling Structural Breaks: Forecasting Indonesian Export," Forecasting, MDPI, vol. 6(1), pages 1-18, February.
    15. Xiaojie Xu, 2017. "Short-run price forecast performance of individual and composite models for 496 corn cash markets," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(14), pages 2593-2620, October.
    16. Brian Goff, 2006. "Supreme Court consensus and dissent: Estimating the role of the selection screen," Public Choice, Springer, vol. 127(3), pages 367-383, June.
    17. David F. Hendry, 2002. "Forecast Failure, Expectations Formation and the Lucas Critique," Annals of Economics and Statistics, GENES, issue 67-68, pages 21-40.
    18. Clements, Michael P., 2002. "Comments on 'The state of macroeconomic forecasting'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 469-482, December.
    19. Neil R. Ericsson, 2017. "How Biased Are U.S. Government Forecasts of the Federal Debt?," International Finance Discussion Papers 1189, Board of Governors of the Federal Reserve System (U.S.).
    20. Geoffrey Shuetrim & Christopher Thompson, 2003. "The Implications of Uncertainty for Monetary Policy," The Economic Record, The Economic Society of Australia, vol. 79(246), pages 370-379, September.
    21. Todd E. Clark & Michael W. McCracken, 2008. "Averaging forecasts from VARs with uncertain instabilities," Working Papers 2008-030, Federal Reserve Bank of St. Louis.
    22. Medel, Carlos A., 2014. "A Comparison Between Direct and Indirect Seasonal Adjustment of the Chilean GDP 1986-2009 with X-12-ARIMA," MPRA Paper 57053, University Library of Munich, Germany.
    23. Berta, P. & Lovaglio, P.G. & Paruolo, P. & Verzillo, S., 2020. "Real Time Forecasting of Covid-19 Intensive Care Units demand," Health, Econometrics and Data Group (HEDG) Working Papers 20/16, HEDG, c/o Department of Economics, University of York.
    24. 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.
    25. Huang, Tao & Fildes, Robert & Soopramanien, Didier, 2019. "Forecasting retailer product sales in the presence of structural change," European Journal of Operational Research, Elsevier, vol. 279(2), pages 459-470.
    26. Corradi, Valentina & Swanson, Norman R. & Olivetti, Claudia, 2001. "Predictive ability with cointegrated variables," Journal of Econometrics, Elsevier, vol. 104(2), pages 315-358, September.
    27. Eitrheim, Oyvind & Husebo, Tore Anders & Nymoen, Ragnar, 1999. "Equilibrium-correction vs. differencing in macroeconometric forecasting," Economic Modelling, Elsevier, vol. 16(4), pages 515-544, December.
    28. 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.
    29. Arvid Raknerud, 2001. "A State Space Approach for Estimating VAR Models for Panel Data with Latent Dynamic Components," Discussion Papers 295, Statistics Norway, Research Department.
    30. Engle, Robert F & Smith, Aaron, 1998. "Stochastic Permanent Breaks," University of California at San Diego, Economics Working Paper Series qt99v0s0zx, Department of Economics, UC San Diego.
    31. Neil R. Ericsson, David F. Hendry & Kevin M. Prestiwch, "undated". "The UK Demand for Broad Money over the Long run," Economics Papers W29, Economics Group, Nuffield College, University of Oxford.
    32. Basdevant, Olivier, 2000. "An econometric model of the Russian Federation," Economic Modelling, Elsevier, vol. 17(2), pages 305-336, April.
    33. 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.
    34. Lima, Luiz Renato Regis de Oliveira & Issler, João Victor, 2008. "A panel data approach to economic forecasting: the bias-corrected average forecast," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 668, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    35. 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.
    36. Ralf Brüggemann & Helmut Lütkepohl, 2011. "Forecasting Contemporaneous Aggregates with Stochastic Aggregation Weights," Working Paper Series of the Department of Economics, University of Konstanz 2011-23, Department of Economics, University of Konstanz.
    37. Yousuf, Kashif & Ng, Serena, 2021. "Boosting high dimensional predictive regressions with time varying parameters," Journal of Econometrics, Elsevier, vol. 224(1), pages 60-87.
    38. Marcellino, Massimiliano & Carriero, Andrea & Clark, Todd, 2011. "Bayesian VARs: Specification Choices and Forecast Accuracy," CEPR Discussion Papers 8273, C.E.P.R. Discussion Papers.
    39. Clements, M.P. & Smith, J., 1997. "Forecasting Seasonal UK Consumption Components," The Warwick Economics Research Paper Series (TWERPS) 487, University of Warwick, Department of Economics.
    40. Muhammad Jahanzeb Malik & Muhammad Nadim Hanif, 2019. "Learning from Errors While Forecasting Inflation: A Case for Intercept Correction," International Econometric Review (IER), Econometric Research Association, vol. 11(1), pages 24-38, April.
    41. Pablo Guerrón-Quintana & Molin Zhong, 2017. "Macroeconomic Forecasting in Times of Crises," Finance and Economics Discussion Series 2017-018, Board of Governors of the Federal Reserve System (U.S.).
    42. Irfan Civcir, 2003. "The Monetary Models of the Turkish Lira/U.S. Dollar Exchange Rate: Long-run Relationships, Short-run Dynamics, and Forecasting," Eastern European Economics, Taylor & Francis Journals, vol. 41(6), pages 43-63, January.
    43. Wagatha, Matthias, 2007. "Integration, Kointegration und die Langzeitprognose von Kreditausfallzyklen [Integration, Cointegration and Long-Horizont Forecasting of Credit-Default-Cycles]," MPRA Paper 8602, University Library of Munich, Germany.
    44. Christopher G. Gibbs & Andrey L. Vasnev, 2017. "Conditionally Optimal Weights and Forward-Looking Approaches to Combining Forecasts," Discussion Papers 2017-10, School of Economics, The University of New South Wales.
    45. Michael P. Clements & Marianne Sensier, 2003. "Asymmetric output‐gap effects in Phillips Curve and mark‐up pricing models: Evidence for the US and the UK," Scottish Journal of Political Economy, Scottish Economic Society, vol. 50(4), pages 359-374, September.
    46. Rebecca A Emerson & David Hendry, 1994. "An evaluation of forecasting using leading indicators," Economics Papers 5., Economics Group, Nuffield College, University of Oxford.
    47. Lazar, Emese & Wang, Shixuan & Xue, Xiaohan, 2023. "Loss function-based change point detection in risk measures," European Journal of Operational Research, Elsevier, vol. 310(1), pages 415-431.
    48. Neil R. Ericsson & David F. Hendry & Kevin M. Prestwich, 1998. "The Demand for Broad Money in the United Kingdom, 1878–1993," Scandinavian Journal of Economics, Wiley Blackwell, vol. 100(1), pages 289-324, March.
    49. Dellas, Harris & Gibson, Heather D. & Hall, Stephen G. & Tavlas, George S., 2018. "The macroeconomic and fiscal implications of inflation forecast errors," Journal of Economic Dynamics and Control, Elsevier, vol. 93(C), pages 203-217.
    50. 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.
    51. Ricardo Mestre & Peter McAdam, 2011. "Is forecasting with large models informative? Assessing the role of judgement in macroeconomic forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(3), pages 303-324, April.
    52. Deskar-Škrbić, Milan & Kotarac, Karlo & Kunovac, Davor, 2020. "The third round of euro area enlargement: Are the candidates ready?," Journal of International Money and Finance, Elsevier, vol. 107(C).
    53. Galvao, Ana Beatriz & Garratt, Anthony & Mitchell, James, 2020. "Does Judgment Improve Macroeconomic Density Forecasts?," EMF Research Papers 33, Economic Modelling and Forecasting Group.
    54. Aaron Smith, 2005. "Forecasting in the presence of level shifts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(8), pages 557-574.
    55. Lance J. Bachmeier & Norman R. Swanson, 2003. "Predicting Inflation: Does The Quantity Theory Help?," Departmental Working Papers 200317, Rutgers University, Department of Economics.
    56. Giorgio Valente & Lucio Sarno, 2005. "Modelling and forecasting stock returns: exploiting the futures market, regime shifts and international spillovers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(3), pages 345-376.
    57. Christopher G. Gibbs, 2015. "Overcoming the Forecast Combination Puzzle: Lessons from the Time-Varying Effciency of Phillips Curve Forecasts of U.S. Inflation," Discussion Papers 2015-09, School of Economics, The University of New South Wales.
    58. Kloek, T., 1998. "Loss development forecasting models: an econometrician's view," Insurance: Mathematics and Economics, Elsevier, vol. 23(3), pages 251-261, December.
    59. Brian Goff, 2005. "Supreme Court consensus and dissent: Estimating the role of the selection screen," Public Choice, Springer, vol. 122(3), pages 483-499, March.
    60. David Hendry, 2000. "A General Forecast-error Taxonomy," Econometric Society World Congress 2000 Contributed Papers 0608, Econometric Society.
    61. 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.
    62. Richard H. Clarida & Lucio Sarno & Mark P. Taylor & Giorgio Valente, 2006. "The Role of Asymmetries and Regime Shifts in the Term Structure of Interest Rates," The Journal of Business, University of Chicago Press, vol. 79(3), pages 1193-1224, May.
    63. 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.
    64. Scott Moss, 2007. "Alternative Approaches to the Empirical Validation of Agent-Based Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(1), pages 1-5.
    65. 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.
    66. Peter Spencer & Zhuoshi Liu, "undated". "An Open-Economy Macro-Finance Model of Internatinal Interdependence: The OECD, US and the UK," Discussion Papers 09/16, Department of Economics, University of York.
    67. Meade, Nigel, 2010. "Oil prices -- Brownian motion or mean reversion? A study using a one year ahead density forecast criterion," Energy Economics, Elsevier, vol. 32(6), pages 1485-1498, November.
    68. 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.
    69. Hall, Stephen & Mizon, Grayham E. & Welfe, Aleksander, 2000. "Modelling economies in transition: an introduction," Economic Modelling, Elsevier, vol. 17(3), pages 339-357, August.
    70. Banerjee, Anindya & Urga, Giovanni, 2005. "Modelling structural breaks, long memory and stock market volatility: an overview," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 1-34.
    71. Carlos Medel, 2021. "Searching for the Best Inflation Forecasters within a Consumer Perceptions Survey: Microdata Evidence from Chile," Working Papers Central Bank of Chile 899, Central Bank of Chile.
    72. 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.
    73. Basdevant, Olivier & Hall, Stephen G., 2002. "The 1998 Russian crisis: could the exchange rate volatility have predicted it?," Journal of Policy Modeling, Elsevier, vol. 24(2), pages 151-168, May.
    74. Jeronymo Marcondes Pinto & Emerson Fernandes Marçal, 2023. "An artificial intelligence approach to forecasting when there are structural breaks: a reinforcement learning-based framework for fast switching," Empirical Economics, Springer, vol. 65(4), pages 1729-1759, October.
    75. Clements, Michael P. & Hendry, David F., 1997. "An empirical study of seasonal unit roots in forecasting," International Journal of Forecasting, Elsevier, vol. 13(3), pages 341-355, September.
    76. 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.
    77. Tommy Wu & Michael Cheng & Ken Wong, 2017. "Bayesian analysis of Hong Kong's housing price dynamics," Pacific Economic Review, Wiley Blackwell, vol. 22(3), pages 312-331, August.
    78. Kyriazi, Foteini & Thomakos, Dimitrios D. & Guerard, John B., 2019. "Adaptive learning forecasting, with applications in forecasting agricultural prices," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1356-1369.
    79. Lin, Jilei & Eck, Daniel J., 2021. "Minimizing post-shock forecasting error through aggregation of outside information," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1710-1727.
    80. David Hendry & Grayham E. Mizon, 2001. "Forecasting in the Presence of Structural Breaks and Policy Regime Shifts," Economics Papers 2002-W12, Economics Group, Nuffield College, University of Oxford.
    81. Wei, Jie & Zhang, Yonghui, 2020. "A time-varying diffusion index forecasting model," Economics Letters, Elsevier, vol. 193(C).
    82. William Larson, 2015. "Forecasting an Aggregate in the Presence of Structural Breaks in the Disaggregates," Working Papers 2015-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    83. David Hendry & Michael P. Clements, 2010. "Forecasting from Mis-specified Models in the Presence of Unanticipated Location Shifts," Economics Series Working Papers 484, University of Oxford, Department of Economics.
    84. Clements, Michael P. & Hendry, David F., 1998. "Forecasting economic processes," International Journal of Forecasting, Elsevier, vol. 14(1), pages 111-131, March.

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    Cited by:

    1. Josef Baumgartner, 2008. "Die Preistransmission entlang der Wertschöpfungskette in Österreich für ausgewählte Produktgruppen," WIFO Studies, WIFO, number 33139.
    2. Igor Masten & Massimiliano Marcellino & Anindya Banerjeey, 2009. "Forecasting with Factor-augmented Error Correction Models," RSCAS Working Papers 2009/32, European University Institute.
    3. Mihaela Bratu (Simionescu), 2013. "How to Improve the SPF Forecasts?," Acta Universitatis Danubius. OEconomica, Danubius University of Galati, issue 9(2), pages 153-165, April.
    4. David Hendry & Andrew B. Martinez, 2016. "Evaluating Multi-Step System Forecasts with Relatively Few Forecast-Error Observations," Economics Series Working Papers 784, University of Oxford, Department of Economics.
    5. Fakhri J. Hasanov & Lester C. Hunt & Ceyhun I. Mikayilov, 2016. "Modeling and Forecasting Electricity Demand in Azerbaijan Using Cointegration Techniques," Energies, MDPI, vol. 9(12), pages 1-31, December.
    6. Ericsson, Neil R., 2017. "Economic forecasting in theory and practice: An interview with David F. Hendry," International Journal of Forecasting, Elsevier, vol. 33(2), pages 523-542.
    7. Berument, Hakan & Dogan, Nukhet & Tansel, Aysit, 2008. "Macroeconomic Policy and Unemployment by Economic Activity: Evidence from Turkey," IZA Discussion Papers 3461, Institute of Labor Economics (IZA).
    8. Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2014. "Forecasting UK GDP growth and inflation under structural change. A comparison of models with time-varying parameters," International Journal of Forecasting, Elsevier, vol. 30(1), pages 129-143.
    9. Sajjad Faraji Dizaji & Mohammad Reza Farzanegan & Alireza Naghavi, 2015. "Political Institutions and Government Spending Behavior: Theory and Evidence from Iran," Development Working Papers 381, Centro Studi Luca d'Agliano, University of Milano.
    10. Barakchian , Seyed Mahdi, 2012. "Implications of Cointegration for Forecasting: A Review and an Empirical Analysis," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 7(1), pages 87-118, October.
    11. Tom Engsted & Niels Haldrup & Boriss Siliverstovs, 2004. "Long-run forecasting in multicointegrated systems," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(5), pages 315-335.
    12. Seema Narayan & Russell Smyth, 2015. "The Financial Econometrics of Price Discovery and Predictability," Monash Economics Working Papers 06-15, Monash University, Department of Economics.
    13. Giacomini, Raffaella, 2014. "Economic theory and forecasting: lessons from the literature," CEPR Discussion Papers 10201, C.E.P.R. Discussion Papers.
    14. Anderson, Richard G. & Hoffman, Dennis L. & Rasche, Robert H., 2002. "A vector error-correction forecasting model of the US economy," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 569-598, December.
    15. Hiroaki Chigira & Taku Yamamoto, 2009. "Forecasting in large cointegrated processes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 631-650.
    16. David Hendry & John Muellbauer, 2017. "The future of macroeconomics: Macro theory and models at the Bank of England," Economics Series Working Papers 832, University of Oxford, Department of Economics.
    17. Patrick BISCIARI & Alain DURRE & Alain NYSSENS, 2003. "Stock Market Valuation In The United States," Finance 0312011, University Library of Munich, Germany.
    18. Mohammad Reza FARZANEGAN & Gunther MARKWARDT, 2008. "The Effects of Oil Price Shocks on the Iranian Economy," EcoMod2008 23800037, EcoMod.
    19. Luca Nocciola, "undated". "Finite sample forecast properties and window length under breaks in cointegrated systems," Discussion Papers 19/07, University of Nottingham, Granger Centre for Time Series Econometrics.
    20. 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.
    21. Sajjad Faraji Dizaji, 2019. "Trade openness, political institutions, and military spending (evidence from lifting Iran’s sanctions)," Empirical Economics, Springer, vol. 57(6), pages 2013-2041, December.
    22. Richard G. Anderson & Dennis L. Hoffman & Robert H. Rasche, 2001. "A vector error correction forecasting model of the U.S. economy," Working Papers 1998-008, Federal Reserve Bank of St. Louis.
    23. Theologos Pantelidis & Nikitas Pittis, 2009. "Estimation and forecasting in first-order vector autoregressions with near to unit roots and conditional heteroscedasticity," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(7), pages 612-630.
    24. Wani, M.H. & Paul, Ranjit Kumar & Bazaz, Naseer H. & Manzoor, M., 2015. "Market integration and Price Forecasting of Apple in India," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 70(2), pages 1-13.
    25. Roseline Nyakerario Misati & Esman Morekwa Nyamongo & Isaac Mwangi, 2013. "Commodity price shocks and inflation in a net oil-importing economy," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 37(2), pages 125-148, June.
    26. Dizaji, Sajjad Faraji & Murshed, Syed Mansoob, 2024. "External arms embargoes and their implications for government expenditure, democracy and internal conflict," World Development, Elsevier, vol. 173(C).
    27. Neil R. Ericsson, 2021. "Dynamic Econometrics in Action: A Biography of David F. Hendry," International Finance Discussion Papers 1311, Board of Governors of the Federal Reserve System (U.S.).
    28. Kuo, Chen-Yin, 2016. "Does the vector error correction model perform better than others in forecasting stock price? An application of residual income valuation theory," Economic Modelling, Elsevier, vol. 52(PB), pages 772-789.
    29. Guillermo Carlomagno & Antoni Espasa, 2021. "Discovering Specific Common Trends in a Large Set of Disaggregates: Statistical Procedures, their Properties and an Empirical Application," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(3), pages 641-662, June.
    30. Shahzad, Syed Jawad Hussain & Ahmed, Tanveer & Rehman, Mobeen Ur & Zakaria, Muhammad, 2014. "Relationship between Developed, Emerging and South Asian Equity Markets: Empirical Evidence with a Multivariate Framework Analysis," MPRA Paper 60398, University Library of Munich, Germany.
    31. David F. Hendry, 2020. "First in, First out: Econometric Modelling of UK Annual CO_2 Emissions, 1860–2017," Economics Papers 2020-W02, Economics Group, Nuffield College, University of Oxford.
    32. Farzanegan, Mohammad Reza & Hassan, Mai & Badreldin, Ahmed Mohamed, 2020. "Economic liberalization in Egypt: A way to reduce the shadow economy?," Journal of Policy Modeling, Elsevier, vol. 42(2), pages 307-327.
    33. Michael P. Clements, 2014. "Long-Run Restrictions and Survey Forecasts of Output, Consumption and Investment," ICMA Centre Discussion Papers in Finance icma-dp2014-02, Henley Business School, University of Reading.
    34. Bratu Simionescu Mihaela, 2012. "Variables Aggregation-Source of Uncertainty in Forecasting," Scientific Annals of Economics and Business, Sciendo, vol. 59(2), pages 1-13, December.
    35. Mohammad Reza Farzanegan, 2014. "Military Spending and Economic Growth: The Case of Iran," Defence and Peace Economics, Taylor & Francis Journals, vol. 25(3), pages 247-269, June.
    36. Mohammad Reza Farzanegan & Mai Hassan, 2017. "The Impact of Economic Globalization on the Shadow Economy in Egypt," CESifo Working Paper Series 6424, CESifo.
    37. Shahzad, Syed Jawad Hussain & Zakaria, Muhammad & Rehman, Mobeen ur & Ahmed, Tanveer & Khalid, Saniya, 2014. "Co-Movement of Pakistan Stock Exchange with India, S&P 500 and Nikkei 225: A Time-frequency (Wavelets) Analysis," MPRA Paper 60579, University Library of Munich, Germany.
    38. Mark E. Wohar & David E. Rapach, 2007. "Forecasting the recent behavior of US business fixed investment spending: an analysis of competing models This is a significantly revised version of our previous paper, 'Forecasting US Business Fixed ," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(1), pages 33-51.
    39. Tian Zhao, 2012. "Firm size, information acquisition and price efficiency," Quantitative Finance, Taylor & Francis Journals, vol. 12(10), pages 1599-1614, October.
    40. Andrew Martinez, 2017. "Testing for Differences in Path Forecast Accuracy: Forecast-Error Dynamics Matter," Working Papers (Old Series) 1717, Federal Reserve Bank of Cleveland.
    41. Raffaella Giacomini, 2014. "Economic theory and forecasting: lessons from the literature," CeMMAP working papers 41/14, Institute for Fiscal Studies.
    42. Peña, Daniel & Poncela, Pilar, 2000. "Forecasting with nostationary dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS 9959, Universidad Carlos III de Madrid. Departamento de Estadística.
    43. Rockie U Kei Kuok & Tay T.R. Koo & Christine Lim, 2024. "Air transport capacity and tourism demand: A panel cointegration approach with cross-sectionally augmented autoregressive distributed lag (CS-ARDL) model," Tourism Economics, , vol. 30(3), pages 702-727, May.
    44. Bewley, Ronald, 2002. "Forecast accuracy, coefficient bias and Bayesian vector autoregressions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 59(1), pages 163-169.
    45. Sajjad Faraji Dizaji & Peter A G van Bergeijk, 2013. "Potential early phase success and ultimate failure of economic sanctions," Journal of Peace Research, Peace Research Institute Oslo, vol. 50(6), pages 721-736, November.
    46. Wang, Yudong & Wu, Chongfeng & Yang, Li, 2014. "Oil price shocks and agricultural commodity prices," Energy Economics, Elsevier, vol. 44(C), pages 22-35.
    47. Wang, Zijun & Bessler, David A, 2002. "The Homogeneity Restriction and Forecasting Performance of VAR-Type Demand Systems: An Empirical Examination of US Meat Consumption," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(3), pages 193-206, April.
    48. Zahra Zarepour, 2022. "Short- and long-run macroeconomic impacts of the 2010 Iranian energy subsidy reform," SN Business & Economics, Springer, vol. 2(10), pages 1-32, October.
    49. Tsangyao Chang & Wenshwo Fang & Li-Fang Wen, 2001. "Energy consumption, employment, output, and temporal causality: evidence from Taiwan based on cointegration and error-correction modelling techniques," Applied Economics, Taylor & Francis Journals, vol. 33(8), pages 1045-1056.
    50. Patrick Ologbenla, 2019. "Fiscal Policy and External Shocks in Nigeria," Journal of Economics and Behavioral Studies, AMH International, vol. 11(1), pages 129-138.
    51. Kosei Fukuda, 2011. "Cointegration rank switching model: an application to forecasting interest rates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(5), pages 509-522, August.
    52. Mohammad Reza Farzanegan & Pooya Alaedini & Khayyam Azizimehr, 2017. "Middle Class in Iran: Oil Rents, Modernization, and Political Development," MAGKS Papers on Economics 201756, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).

  82. Clements, Michael P & Hendry, David F, 1995. "Macro-economic Forecasting and Modelling," Economic Journal, Royal Economic Society, vol. 105(431), pages 1001-1013, July.

    Cited by:

    1. Edward Nelson & Kalin Nikolov, 2001. "UK inflation in the 1970s and 1980s: the role of output gap mismeasurement," Bank of England working papers 148, Bank of England.
    2. Klaus Weyerstrass, 2002. "The german stability program: A quantitative assessment," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 30(3), pages 320-334, September.
    3. Bovi, Maurizio, 2013. "Are the representative agent’s beliefs based on efficient econometric models?," Journal of Economic Dynamics and Control, Elsevier, vol. 37(3), pages 633-648.
    4. John Berdell & Animesh Ghoshal, 2015. "US–Mexico border tourism and day trips: an aberration in globalization?," Latin American Economic Review, Springer;Centro de Investigaciòn y Docencia Económica (CIDE), vol. 24(1), pages 1-18, December.
    5. Brissimis, Sophocles N. & Sideris, Dimitris A. & Voumvaki, Fragiska K., 2005. "Testing long-run purchasing power parity under exchange rate targeting," Journal of International Money and Finance, Elsevier, vol. 24(6), pages 959-981, October.
    6. Eitrheim, Oyvind & Husebo, Tore Anders & Nymoen, Ragnar, 1999. "Equilibrium-correction vs. differencing in macroeconometric forecasting," Economic Modelling, Elsevier, vol. 16(4), pages 515-544, December.
    7. Dario Rukelj & Barbara Ulloa, 2011. "Incorporating uncertainties into economic forecasts: an application to forecasting economic activity in Croatia," Financial Theory and Practice, Institute of Public Finance, vol. 35(2), pages 140-170.
    8. Irfan Civcir, 2003. "The Monetary Models of the Turkish Lira/U.S. Dollar Exchange Rate: Long-run Relationships, Short-run Dynamics, and Forecasting," Eastern European Economics, Taylor & Francis Journals, vol. 41(6), pages 43-63, January.
    9. Janine Aron & John Muellbauer & B. Smit, 2004. "A Structural Model of the Inflation Process in South Africa," CSAE Working Paper Series 2004-08, Centre for the Study of African Economies, University of Oxford.
    10. Garcia-Ferrer, Antonio, 1996. "Dynamic econometrics : David F. Hendry, 1995, (Oxford University Press, Oxford), 904 pp., paperback, [UK pound]25.00, ISBN 0-19-828316-4, hardback, [UK pound]50.00, ISBN 0-19-828317-2," International Journal of Forecasting, Elsevier, vol. 12(2), pages 306-308, June.
    11. Peaucelle, Irina, 1996. "Prévisions de court terme pour analyser les réformes en Russie (les)," CEPREMAP Working Papers (Couverture Orange) 9610, CEPREMAP.
    12. Durré, Alain & Evjen, Snorre & Pilegaard, Rasmus, 2003. "Estimating risk premia in money market rates," Working Paper Series 221, European Central Bank.
    13. Škovránek, Tomáš & Podlubny, Igor & Petráš, Ivo, 2012. "Modeling of the national economies in state-space: A fractional calculus approach," Economic Modelling, Elsevier, vol. 29(4), pages 1322-1327.
    14. 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.
    15. Zhang, Mingzhu & He, Changzheng & Gu, Xin & Liatsis, Panos & Zhu, Bing, 2013. "D-GMDH: A novel inductive modelling approach in the forecasting of the industrial economy," Economic Modelling, Elsevier, vol. 30(C), pages 514-520.
    16. Mattias Villani, 2009. "Steady-state priors for vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 630-650.
    17. Clements, Michael P. & Hendry, David F., 1998. "Forecasting economic processes," International Journal of Forecasting, Elsevier, vol. 14(1), pages 111-131, March.

  83. Clements, Michael P, 1995. "Rationality and the Role of Judgement in Macroeconomic Forecasting," Economic Journal, Royal Economic Society, vol. 105(429), pages 410-420, March.

    Cited by:

    1. Clements, Michael P., 2008. "Explanations of the inconsistencies in survey respondents'forecasts," The Warwick Economics Research Paper Series (TWERPS) 870, University of Warwick, Department of Economics.
    2. Philip Hans Franses, 2021. "Modeling Judgment in Macroeconomic Forecasts," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 401-417, December.
    3. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    4. Michael Pedersen, 2024. "Judgment in macroeconomic output growth predictions: Efficiency, accuracy and persistence," Papers 2404.04105, arXiv.org.
    5. Henk Kranendonk & Debby Lanser & P.H. Franses, 2007. "On the optimality of expert-adjusted forecasts," CPB Discussion Paper 92, CPB Netherlands Bureau for Economic Policy Analysis.
    6. Michael Clements, 2016. "Are Macro-Forecasters Essentially The Same? An Analysis of Disagreement, Accuracy and Efficiency," ICMA Centre Discussion Papers in Finance icma-dp2016-08, Henley Business School, University of Reading.
    7. Lawrence, Michael & O'Connor, Marcus, 2000. "Sales forecasting updates: how good are they in practice?," International Journal of Forecasting, Elsevier, vol. 16(3), pages 369-382.
    8. Carl Singleton & J. James Reade & Alsdair Brown, 2018. "Going with your Gut: The (In)accuracy of Forecast Revisions in a Football Score Prediction Game," Working Papers 2018-006, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    9. Masahiro Ashiya, 2006. "Testing the rationality of forecast revisions made by the IMF and the OECD," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(1), pages 25-36.
    10. Michael P. Clements, 2018. "Do Macroforecasters Herd?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(2-3), pages 265-292, March.
    11. Michael P Clements, 2014. "Assessing the Evidence of Macro- Forecaster Herding: Forecasts of Inflation and Output Growth," ICMA Centre Discussion Papers in Finance icma-dp2014-12, Henley Business School, University of Reading.
    12. Dupuy, A., 2009. "An evaluation of the forecast of the indicator of the labour market gap," ROA Technical Report 003, Maastricht University, Research Centre for Education and the Labour Market (ROA).
    13. Patrick Mcallister & Graeme Newell & George Matysiak, 2008. "Agreement and Accuracy in Consensus Forecasts of the UK Commercial Property Market," Journal of Property Research, Taylor & Francis Journals, vol. 25(1), pages 1-22, June.
    14. Gultekin Isiklar & Kajal Lahiri & Prakash Loungani, 2006. "How quickly do forecasters incorporate news? Evidence from cross‐country surveys," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 703-725, September.
    15. Galvao, Ana Beatriz & Garratt, Anthony & Mitchell, James, 2020. "Does Judgment Improve Macroeconomic Density Forecasts?," EMF Research Papers 33, Economic Modelling and Forecasting Group.
    16. Fred Joutz & Michael P. Clements & Herman O. Stekler, 2007. "An evaluation of the forecasts of the federal reserve: a pooled approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 121-136.
    17. Sebastiano Manzan, 2011. "Differential Interpretation in the Survey of Professional Forecasters," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(5), pages 993-1017, August.
    18. 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.
    19. David F. Hendry & Michael P. Clements, 1994. "Can Econometrics Improve Economic Forecasting?," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 130(III), pages 267-298, September.
    20. Jakab M., Zoltán & Kovács, Mihály András & Kiss, Gergely, 2006. "Mit tanultunk?. A jegybanki előrejelzések szerepe az inflációs cél követésének első öt évében Magyarországon [What are we studying?. The role of central-bank forecasts in Hungarian inflation target," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(12), pages 1101-1134.
    21. Ulrich K. Müller & Gebhard Kirchgässner, 2006. "Are forecasters reluctant to revise their predictions? Some German evidence," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(6), pages 401-413.
    22. Dupuy, A., 2005. "An evaluation of labour market forecasts by type of education and occupation for 2002," ROA Working Paper 1E, Maastricht University, Research Centre for Education and the Labour Market (ROA).
    23. Lawrence, Michael & Goodwin, Paul & O'Connor, Marcus & Onkal, Dilek, 2006. "Judgmental forecasting: A review of progress over the last 25 years," International Journal of Forecasting, Elsevier, vol. 22(3), pages 493-518.
    24. Xiao, Jinzhi & Lence, Sergio H. & Hart, Chad, 2014. "Usda And Private Analysts' Forecasts Of Ending Stocks: How Good Are They?," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170642, Agricultural and Applied Economics Association.
    25. Vuchelen, Jef & Gutierrez, Maria-Isabel, 2005. "A direct test of the information content of the OECD growth forecasts," International Journal of Forecasting, Elsevier, vol. 21(1), pages 103-117.

  84. David F. Hendry & Michael P. Clements, 1994. "Can Econometrics Improve Economic Forecasting?," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 130(III), pages 267-298, September.

    Cited by:

    1. Ibrahim A. Elbadawi & Raimundo Soto, "undated". "Real Exchange Rates and Macroeconomic Adjustment in Sub-Sahara Africa and Other Developing Countries," ILADES-UAH Working Papers inv093, Universidad Alberto Hurtado/School of Economics and Business.

  85. Clements, Michael P. & Mizon, Grayham E., 1991. "Empirical analysis of macroeconomic time series : VAR and structural models," European Economic Review, Elsevier, vol. 35(4), pages 887-917, May.

    Cited by:

    1. Dimitrios Sideris, 2004. "Testing for Long-Run PPP in a System Context: Evidence for the US, Germany and Japan," Working Papers 19, Bank of Greece.
    2. Mirosław Bełej, 2022. "Does Google Trends Show the Strength of Social Interest as a Predictor of Housing Price Dynamics?," Sustainability, MDPI, vol. 14(9), pages 1-14, May.
    3. Bernd Hayo, 2000. "The demand for money in Austria," Empirical Economics, Springer, vol. 25(4), pages 581-603.
    4. Farrokh Nourzad, 1997. "The Short-and Long-Run Relationships Between the Exchange Rate of the Dollar and Producer Prices in the U.S," International Economic Journal, Taylor & Francis Journals, vol. 11(2), pages 59-71.
    5. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    6. Calderón Villarreal, Cuauhtémoc & Cuevas, Víctor M., 2019. "Industrial growth and consumer goods inflation in Mexico: an econometric analysis," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), December.
    7. 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.
    8. Urbain, Jean-Pierre, 1995. "Partial versus full system modelling of cointegrated systems an empirical illustration," Journal of Econometrics, Elsevier, vol. 69(1), pages 177-210, September.
    9. Mr. Francis Y Kumah & Mr. John J Matovu, 2005. "Commodity Price Shocks and the Oddson Fiscal Performance," IMF Working Papers 2005/171, International Monetary Fund.
    10. Thórarinn G. Pétursson, 2002. "Wage and price formation in a small open Economy: Evidence from Iceland," Economics wp16_thorarinn, Department of Economics, Central bank of Iceland.
    11. 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.
    12. Christian Balcells, 2022. "Determinants of firm boundaries and organizational performance: an empirical investigation of the Chilean truck market," Journal of Evolutionary Economics, Springer, vol. 32(2), pages 423-461, April.
    13. Matteo Manera, 2005. "Modeling Factor Demands with SEM and VAR: An Empirical Comparison," Working Papers 2005.47, Fondazione Eni Enrico Mattei.
    14. Norbert Fiess & Ronald MacDonald, 1999. "Technical Analysis in the Foreign Exchange Market: A Cointegration-Based Approach," Multinational Finance Journal, Multinational Finance Journal, vol. 3(3), pages 147-172, September.
    15. Alexander Herzog-Stein & Camille Logeay, 2019. "Short-Term macroeconomic evaluation of the German minimum wage with a VAR/VECM," IMK Working Paper 197-2019, IMK at the Hans Boeckler Foundation, Macroeconomic Policy Institute.
    16. Padma Desai, 2006. "Why Is Russian GDP Growth Slowing?," American Economic Review, American Economic Association, vol. 96(2), pages 342-347, May.
    17. Bhattacharya, Prasad S. & Thomakos, Dimitrios D., 2006. "Forecasting industry-level CPI and PPI inflation: does exchange rate pass-through matter?," Working Papers eco_2006_10, Deakin University, Department of Economics.
    18. Hilde Christiane Bjørnland, 1996. "Sources of Business Cycles in Energy Producing Economies - The case of Norway and United Kingdom," Discussion Papers 179, Statistics Norway, Research Department.
    19. Pillai N., Vijayamohanan, 2008. "In Quest of Truth: The War of Methods in Economics," MPRA Paper 8866, University Library of Munich, Germany.
    20. Brissimis, Sophocles N. & Sideris, Dimitris A. & Voumvaki, Fragiska K., 2005. "Testing long-run purchasing power parity under exchange rate targeting," Journal of International Money and Finance, Elsevier, vol. 24(6), pages 959-981, October.
    21. Willem J.H. van Groenendaal, 2011. "Energy Model and Policy Advice: The Effect of Model Choice," Chapters, in: Raymond J.G.M. Florax & Henri L.F. de Groot & Peter Mulder (ed.), Improving Energy Efficiency through Technology, chapter 11, Edward Elgar Publishing.
    22. Eric Heyer & Hervé Le Bihan & Frédéric Lerais, 2000. "Relation de Phillips, boucle prix-salaire : une estimation par la méthode de Johansen," Économie et Prévision, Programme National Persée, vol. 146(5), pages 43-60.
    23. 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.
    24. Pritha Mitra, 2006. "Has Government Investment Crowded Out Private Investment in India?," American Economic Review, American Economic Association, vol. 96(2), pages 337-341, May.
    25. 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.
    26. Ballabriga, Fernando & Sebastian, Miguel & Valles, Javier, 1999. "European asymmetries," Journal of International Economics, Elsevier, vol. 48(2), pages 233-253, August.
    27. Giannini, Carlo & Lanzarotti, Antonio & Seghelini, Mario, 1995. "A traditional interpretation of macroeconomic fluctuations: The case of Italy," European Journal of Political Economy, Elsevier, vol. 11(1), pages 131-155, March.
    28. Irfan Civcir, 2003. "The Monetary Models of the Turkish Lira/U.S. Dollar Exchange Rate: Long-run Relationships, Short-run Dynamics, and Forecasting," Eastern European Economics, Taylor & Francis Journals, vol. 41(6), pages 43-63, January.
    29. Podivinsky, Jan M., 1998. "Testing misspecified cointegrating relationships," Economics Letters, Elsevier, vol. 60(1), pages 1-9, July.
    30. Ramón López & Gustavo Anríquez, 2004. "Poverty and Agricultural Growth: Chile in the 1990s," The Electronic Journal of Agricultural and Development Economics, Food and Agriculture Organization of the United Nations, vol. 1(1), pages 6-24.
    31. Wilson Luiz Rotatori & Jan M Podivinsky, 2007. "Dynamic Macroeconometric Modelling: Evidence on the Brazilian Monetary System," EcoMod2007 23900078, EcoMod.
    32. Pham The Anh, 2007. "Nominal Rigidities and The Real Effects of Monetary Policy in a Structural VAR Model," Working Papers 06, Development and Policies Research Center (DEPOCEN), Vietnam.
    33. International Monetary Fund, 2007. "Vietnam: Selected Issues," IMF Staff Country Reports 2007/385, International Monetary Fund.
    34. Chris Stewart, 2003. "An International Comparison Of Long-Run Consumer Behaviour," European Research Studies Journal, European Research Studies Journal, vol. 0(1-2), pages 145-168, January -.
    35. Andre Jungmittag & Paul J.J. Welfens, 2004. "Politikberatung und empirische Wirtschaftsforschung: Entwicklungen, Probleme, Optionen für mehr Rationalität in der Wirtschaftspolitik," EIIW Discussion paper disbei121, Universitätsbibliothek Wuppertal, University Library.
    36. Arize, A. C. & Malindretos, John & Grivoyannis, Elias C., 2005. "Inflation-rate volatility and money demand: Evidence from less developed countries," International Review of Economics & Finance, Elsevier, vol. 14(1), pages 57-80.
    37. Wilson Luiz Rotatori, 2006. "Dynamic Structural Models And The High Inflation Period In Brazil: Modelling The Monetary System," Anais do XXXIV Encontro Nacional de Economia [Proceedings of the 34th Brazilian Economics Meeting] 44, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    38. Sophia Dimelis, 1997. "Cyclical and causal relations between real wages and employment in the EU," Applied Economics, Taylor & Francis Journals, vol. 29(3), pages 311-324.
    39. 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.
    40. Ronald MacDonald & Ian W. Marsh, 1997. "On Fundamentals And Exchange Rates: A Casselian Perspective," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 655-664, November.
    41. Paulo Chahuara, 2020. "Análisis Empírico de la Relación entre Competencia e Inversión en el Servicio de Telefonía Móvil Peruano," Documentos de Trabajo 42, OSIPTEL.
    42. Moid U. Ahmad & Hetti Arachchige Gamini Premaratne, 2018. "Effect of Low and Negative Interest Rates: Evidence from Indian and Sri Lankan Economies," Business Perspectives and Research, , vol. 6(2), pages 90-99, July.
    43. Boswijk, H. Peter, 1995. "Conditional and structural error correction models reply," Journal of Econometrics, Elsevier, vol. 69(1), pages 173-175, September.
    44. Podivinsky, Jan M., 1992. "Small sample properties of tests of linear restrictions on cointegrating vectors and their weights," Economics Letters, Elsevier, vol. 39(1), pages 13-18, May.
    45. Troug, Haytem & Murray, Matt, 2015. "The Effects of Asymmetric Shocks in Oil Prices on the Performance of the Libyan Economy," MPRA Paper 68705, University Library of Munich, Germany.
    46. 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.

Chapters

  1. Michael P. Clements & David I. Harvey, 2009. "Forecast Combination and Encompassing," Palgrave Macmillan Books, in: Terence C. Mills & Kerry Patterson (ed.), Palgrave Handbook of Econometrics, chapter 4, pages 169-198, Palgrave Macmillan.

    Cited by:

    1. Narayan Kundan Kishor, 2021. "Forecasting real‐time economic activity using house prices and credit conditions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 213-227, March.
    2. David I. Harvey & Stephen J. Leybourne & Emily J. Whitehouse, 2017. "Forecast evaluation tests and negative long-run variance estimates in small samples," Discussion Papers 17/03, University of Nottingham, Granger Centre for Time Series Econometrics.
    3. McMillan, David G., 2019. "Stock return predictability: Using the cyclical component of the price ratio," Research in International Business and Finance, Elsevier, vol. 48(C), pages 228-242.
    4. Timo Dimitriadis & Xiaochun Liu & Julie Schnaitmann, 2020. "Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary," Papers 2009.07341, arXiv.org.
    5. 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.
    6. 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.
    7. Ulrich Gunter, 2021. "Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests," Forecasting, MDPI, vol. 3(4), pages 1-36, November.
    8. Carlos Fonseca Marinheiro, 2010. "Fiscal sustainability and the accuracy of macroeconomic forecasts: do supranational forecasts rather than government forecasts make a difference?," GEMF Working Papers 2010-07, GEMF, Faculty of Economics, University of Coimbra.
    9. Clements, Michael P. & Harvey, David I., 2011. "Combining probability forecasts," International Journal of Forecasting, Elsevier, vol. 27(2), pages 208-223.
    10. 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.
    11. Timo Dimitriadis & Julie Schnaitmann, 2019. "Forecast Encompassing Tests for the Expected Shortfall," Papers 1908.04569, arXiv.org, revised Aug 2020.
    12. David G. McMillan, 2021. "Predicting GDP growth with stock and bond markets: Do they contain different information?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3651-3675, July.

  2. Clements, Michael P. & Hendry, David F., 2006. "Forecasting with Breaks," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 12, pages 605-657, Elsevier.

    Cited by:

    1. Josef Baumgartner, 2008. "Die Preistransmission entlang der Wertschöpfungskette in Österreich für ausgewählte Produktgruppen," WIFO Studies, WIFO, number 33139.
    2. Pesaran, M.H. & Pick, A., 2008. "Forecasting Random Walks Under Drift Instability," Cambridge Working Papers in Economics 0814, Faculty of Economics, University of Cambridge.
    3. 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.
    4. Barbara Rossi, 2011. "Advances in Forecasting Under Instability," Working Papers 11-20, Duke University, Department of Economics.
    5. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2015. "Robust approaches to forecasting," International Journal of Forecasting, Elsevier, vol. 31(1), pages 99-112.
    6. Jari Hännikäinen, 2014. "Multi-step forecasting in the presence of breaks," Working Papers 1494, Tampere University, Faculty of Management and Business, Economics.
    7. Sjoerd van den Hauwe & Richard Paap & Dick J.C. van Dijk, 2011. "An Alternative Bayesian Approach to Structural Breaks in Time Series Models," Tinbergen Institute Discussion Papers 11-023/4, Tinbergen Institute.
    8. Ericsson, Neil R., 2017. "Economic forecasting in theory and practice: An interview with David F. Hendry," International Journal of Forecasting, Elsevier, vol. 33(2), pages 523-542.
    9. Massimiliano Marcellino, "undated". "Further Results on MSFE Encompassing," Working Papers 143, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    10. Mehmet Pinar & Thanasis Stengos & M. Ege Yazgan, 2018. "Quantile forecast combination using stochastic dominance," Empirical Economics, Springer, vol. 55(4), pages 1717-1755, December.
    11. Clements, Michael P., 2010. "Why are survey forecasts superior to model forecasts?," The Warwick Economics Research Paper Series (TWERPS) 954, University of Warwick, Department of Economics.
    12. WAN, Shui-Ki & WANG, Shin-Huei & WOO, Chi-Keung, 2012. "Total tourist arrival forecast: aggregation vs. disaggregation," LIDAM Discussion Papers CORE 2012039, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    13. Hendry, David F. & Hubrich, Kirstin, 2010. "Combining disaggregate forecasts or combining disaggregate information to forecast an aggregate," Working Paper Series 1155, European Central Bank.
    14. Liudas Giraitis & George Kapetanios & Simon Price, 2012. "Adaptive Forcasting in the Presence of Recent and Ongoing Structural Change," CAMA Working Papers 2012-14, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    15. Tae-Hwy Lee & Shahnaz Parsaeian & Aman Ullah, 2022. "Forecasting under Structural Breaks Using Improved Weighted Estimation," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202212, University of Kansas, Department of Economics.
    16. Nikolay Robinzonov & Klaus Wohlrabe, 2010. "Freedom of Choice in Macroeconomic Forecasting ," CESifo Economic Studies, CESifo Group, vol. 56(2), pages 192-220, June.
    17. Jiawen Xu & Pierre Perron, 2015. "Forecasting in the presence of in and out of sample breaks," Boston University - Department of Economics - Working Papers Series wp2015-012, Boston University - Department of Economics.
    18. Rapach, David E. & Strauss, Jack K., 2009. "Differences in housing price forecastability across US states," International Journal of Forecasting, Elsevier, vol. 25(2), pages 351-372.
    19. M. Hashem Pesaran & Til Schuermann & L. Vanessa Smith, 2008. "Forecasting economic and financial variables with global VARs," Staff Reports 317, Federal Reserve Bank of New York.
    20. Jennifer Castle & David Hendry & Nicholas W.P. Fawcett, 2008. "Forecasting with Equilibrium-correction Models during Structural Breaks," Economics Series Working Papers 408, University of Oxford, Department of Economics.
    21. 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.
    22. 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.
    23. Fokin, Nikita & Polbin, Andrey, 2019. "A Bivariate Forecasting Model For Russian GDP Under Structural Changes In Monetary Policy and Long-Term Growth," MPRA Paper 95306, University Library of Munich, Germany, revised Apr 2019.
    24. Rapach, David E. & Strauss, Jack K., 2012. "Forecasting US state-level employment growth: An amalgamation approach," International Journal of Forecasting, Elsevier, vol. 28(2), pages 315-327.
    25. 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.
    26. Lima, Luiz Renato Regis de Oliveira & Issler, João Victor, 2008. "A panel data approach to economic forecasting: the bias-corrected average forecast," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 668, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    27. 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.
    28. Kapetanios, George & Labhard, Vincent & Price, Simon, 2008. "Forecast combination and the Bank of England's suite of statistical forecasting models," Economic Modelling, Elsevier, vol. 25(4), pages 772-792, July.
    29. Geweke, John F. & Horowitz, Joel L. & Pesaran, M. Hashem, 2006. "Econometrics: A Bird's Eye View," IZA Discussion Papers 2458, Institute of Labor Economics (IZA).
    30. Jari Hännikäinen, 2016. "Selection of an Estimation Window in the Presence of Data Revisions and Recent Structural Breaks," Working Papers 1692, Tampere University, Faculty of Management and Business, Economics.
    31. Michael P. Clements & Ana Beatriz Galvão, 2011. "Improving Real-time Estimates of Output Gaps and Inflation Trends with Multiple-vintage Models," Working Papers 678, Queen Mary University of London, School of Economics and Finance.
    32. Yi, Yongsheng & Ma, Feng & Zhang, Yaojie & Huang, Dengshi, 2019. "Forecasting stock returns with cycle-decomposed predictors," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 250-261.
    33. Alessandro Casini, 2018. "Tests for Forecast Instability and Forecast Failure under a Continuous Record Asymptotic Framework," Papers 1803.10883, arXiv.org, revised Dec 2018.
    34. Clements, Michael P. & Galvão, Ana Beatriz & Kim, Jae H., 2006. "Quantile Forecasts of Daily Exchange Rate Returns from Forecasts of Realized Volatility," The Warwick Economics Research Paper Series (TWERPS) 777, University of Warwick, Department of Economics.
    35. YiLi Chien & Harold L. Cole & Hanno Lustig, 2014. "Implications of heterogeneity in preferences, beliefs and asset trading technologies for the macroeconomy," Working Papers 2014-14, Federal Reserve Bank of St. Louis.
    36. Pesaran, M.H. & Assenmacher-Wesche, K., 2007. "Assessing forecast uncertainties in a VECX* model for Switzerland: an exercise in forecast combination across models and observation windows," Cambridge Working Papers in Economics 0746, Faculty of Economics, University of Cambridge.
    37. Michael Artis & Massimiliano Marcellino, "undated". "Fiscal Solvency and Fiscal Forecasting in Europe," Working Papers 142, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    38. Rapach, David & Zhou, Guofu, 2013. "Forecasting Stock Returns," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 328-383, Elsevier.
    39. Polito, Vito & Wickens, Mike, 2012. "A model-based indicator of the fiscal stance," European Economic Review, Elsevier, vol. 56(3), pages 526-551.
    40. Timmermann, Allan & Elliott, Graham, 2007. "Economic Forecasting," CEPR Discussion Papers 6158, C.E.P.R. Discussion Papers.
    41. 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.
    42. Antoine Mandel & Amir Sani, 2017. "A Machine Learning Approach to the Forecast Combination Puzzle," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01317974, HAL.
    43. Antoine Mandel & Amir Sani, 2016. "Learning Time-Varying Forecast Combinations," Documents de travail du Centre d'Economie de la Sorbonne 16036, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    44. Clements, Michael P., 2016. "Real-time factor model forecasting and the effects of instability," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 661-675.
    45. Mehmet Pinar & Thanasis Stengos & M. Ege Yazgan, 2012. "Is there an optimal forecast combination? A stochastic dominance approach applied to the forecast combination puzzle," Working Papers 1206, University of Guelph, Department of Economics and Finance.
    46. Kirstin Hubrich & David F. Hendry, 2005. "Forecasting Aggregates by Disaggregates," Computing in Economics and Finance 2005 270, Society for Computational Economics.
    47. Hendry, David F. & Hubrich, Kirstin, 2006. "Forecasting economic aggregates by disaggregates," Working Paper Series 589, European Central Bank.
    48. Ahumada, H. & Cornejo, M., 2016. "Forecasting food prices: The case of corn, soybeans and wheat," International Journal of Forecasting, Elsevier, vol. 32(3), pages 838-848.
    49. Christian Kascha & Francesco Ravazzolo, 2008. "Combining inflation density forecasts," Working Paper 2008/22, Norges Bank.
    50. Uwe Hassler & Marc-Oliver Pohle, 2019. "Forecasting under Long Memory and Nonstationarity," Papers 1910.08202, arXiv.org.
    51. Clements, Michael P, 2012. "Subjective and Ex Post Forecast Uncertainty : US Inflation and Output Growth," The Warwick Economics Research Paper Series (TWERPS) 995, University of Warwick, Department of Economics.
    52. Kaya, Huseyin, 2013. "Forecasting the yield curve and the role of macroeconomic information in Turkey," Economic Modelling, Elsevier, vol. 33(C), pages 1-7.
    53. Davide De Gaetano, 2016. "Forecast Combinations For Realized Volatility In Presence Of Structural Breaks," Departmental Working Papers of Economics - University 'Roma Tre' 0208, Department of Economics - University Roma Tre.
    54. 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.
    55. Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.
    56. Clements, Michael P. & Galvão, Ana Beatriz, 2013. "Forecasting with vector autoregressive models of data vintages: US output growth and inflation," International Journal of Forecasting, Elsevier, vol. 29(4), pages 698-714.
    57. Kapetanios, G. & Tzavalis, E., 2010. "Modeling structural breaks in economic relationships using large shocks," Journal of Economic Dynamics and Control, Elsevier, vol. 34(3), pages 417-436, March.
    58. Davide De Gaetano, 2018. "Forecast Combinations in the Presence of Structural Breaks: Evidence from U.S. Equity Markets," Mathematics, MDPI, vol. 6(3), pages 1-19, March.
    59. G. Bruno & L. Crosilla & P. Margani, 2019. "Inspecting the Relationship Between Business Confidence and Industrial Production: Evidence on Italian Survey Data," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 15(1), pages 1-24, April.
    60. Issler, João Victor & Rodrigues, Claudia Ferreira & Burjack, Rafael, 2013. "Using common features to understand the behavior of metal-commodity prices and forecast them at different horizons," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 735, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    61. Afsaneh Bahrami & Abul Shamsuddin & Katherine Uylangco, 2018. "Out‐of‐sample stock return predictability in emerging markets," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(3), pages 727-750, September.
    62. Clements, Michael P. & Reade, J. James, 2020. "Forecasting and forecast narratives: The Bank of England Inflation Reports," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1488-1500.
    63. Chevillon, Guillaume, 2016. "Multistep forecasting in the presence of location shifts," International Journal of Forecasting, Elsevier, vol. 32(1), pages 121-137.
    64. Pesaran, M. Hashem & Pick, Andreas & Pranovich, Mikhail, 2013. "Optimal forecasts in the presence of structural breaks," Journal of Econometrics, Elsevier, vol. 177(2), pages 134-152.
    65. Yili Chien & Harold Cole & Hanno Lustig, 2016. "Implications of Heterogeneity in Preferences, Beliefs and Asset Trading Technologies in an Endowment Economy," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 20, pages 215-239, April.
    66. Valentina Corradi & Norman Swanson, 2013. "Testing for Structural Stability of Factor Augmented Forecasting Models," Departmental Working Papers 201314, Rutgers University, Department of Economics.
    67. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2013. "Forecasting by factors, by variables, by both or neither?," Journal of Econometrics, Elsevier, vol. 177(2), pages 305-319.
    68. Alexander HARIN, 2014. "Partially Unforeseen Events. Corrections and Correcting Formulae for Forecasts," Expert Journal of Economics, Sprint Investify, vol. 2(2), pages 69-79.
    69. Yoonsuk Lee & B. Wade Brorsen, 2017. "Permanent Breaks and Temporary Shocks in a Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 49(2), pages 255-270, February.
    70. Clements, Michael P. & Kim, Jae H., 2007. "Bootstrap prediction intervals for autoregressive time series," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3580-3594, April.
    71. David Hendry & Grayham E. Mizon, 2001. "Forecasting in the Presence of Structural Breaks and Policy Regime Shifts," Economics Papers 2002-W12, Economics Group, Nuffield College, University of Oxford.
    72. Timmermann, Allan, 2008. "Elusive return predictability," International Journal of Forecasting, Elsevier, vol. 24(1), pages 1-18.
    73. William Larson, 2015. "Forecasting an Aggregate in the Presence of Structural Breaks in the Disaggregates," Working Papers 2015-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    74. David Hendry & Michael P. Clements, 2010. "Forecasting from Mis-specified Models in the Presence of Unanticipated Location Shifts," Economics Series Working Papers 484, University of Oxford, Department of Economics.

Books

  1. Clements, Michael P. & Hendry, David F. (ed.), 2011. "The Oxford Handbook of Economic Forecasting," OUP Catalogue, Oxford University Press, number 9780195398649, Decembrie.

    Cited by:

    1. Katarina Juselius, 2022. "A Theory-Consistent CVAR Scenario for a Monetary Model with Forward-Looking Expectations," Econometrics, MDPI, vol. 10(2), pages 1-15, April.
    2. Salisu, Afees A. & Gupta, Rangan & Demirer, Riza, 2022. "Global financial cycle and the predictability of oil market volatility: Evidence from a GARCH-MIDAS model," Energy Economics, Elsevier, vol. 108(C).
    3. Castle, Jennifer L. & Clements, Michael P. & Hendry, David F., 2015. "Robust approaches to forecasting," International Journal of Forecasting, Elsevier, vol. 31(1), pages 99-112.
    4. Nikolaos Stoupos & Apostolos Kiohos, 2022. "Euro Area: Towards a European Common Bond? – Empirical Evidence from the Sovereign Debt Markets," Journal of Common Market Studies, Wiley Blackwell, vol. 60(4), pages 1019-1046, July.
    5. Ca’ Zorzi, Michele & Kolasa, Marcin & Rubaszek, Michał, 2017. "Exchange rate forecasting with DSGE models," Journal of International Economics, Elsevier, vol. 107(C), pages 127-146.
    6. Hecq, Alain & Jacobs, Jan P.A.M. & Stamatogiannis, Michalis P., 2019. "Testing for news and noise in non-stationary time series subject to multiple historical revisions," Journal of Macroeconomics, Elsevier, vol. 60(C), pages 396-407.
    7. Davide Pettenuzzo & Antonio Gargano & Allan Timmermann, 2014. "Bond Return Predictability: Economic Value and Links to the Macroeconomy," Working Papers 75R, Brandeis University, Department of Economics and International Business School, revised Jul 2016.
    8. Ericsson, Neil R., 2017. "Economic forecasting in theory and practice: An interview with David F. Hendry," International Journal of Forecasting, Elsevier, vol. 33(2), pages 523-542.
    9. Qin, Duo & He, Xinhua, 2012. "Modelling the impact of aggregate financial shocks external to the Chinese economy," BOFIT Discussion Papers 25/2012, Bank of Finland Institute for Emerging Economies (BOFIT).
    10. Jack Fosten, 2016. "Model selection with factors and variables," University of East Anglia School of Economics Working Paper Series 2016-07, School of Economics, University of East Anglia, Norwich, UK..
    11. 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.
    12. Hartmann, Matthias & Dovern, Jonas, 2016. "Forecast Performance, Disagreement, and Heterogeneous Signal-to-Noise Ratios," VfS Annual Conference 2016 (Augsburg): Demographic Change 145925, Verein für Socialpolitik / German Economic Association.
    13. Ricardo Reis, 2017. "Is Something Really Wrong with Macroeconomics?," Discussion Papers 1713, Centre for Macroeconomics (CFM).
    14. Laurent Callot & Johannes Tang Kristensen, 2016. "Regularized Estimation of Structural Instability in Factor Models: The US Macroeconomy and the Great Moderation," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 437-479, Emerald Group Publishing Limited.
    15. Jiang, Yu & Guo, Yongji & Zhang, Yihao, 2017. "Forecasting China's GDP growth using dynamic factors and mixed-frequency data," Economic Modelling, Elsevier, vol. 66(C), pages 132-138.
    16. 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.
    17. Liudas Giraitis & George Kapetanios & Simon Price, 2012. "Adaptive Forcasting in the Presence of Recent and Ongoing Structural Change," CAMA Working Papers 2012-14, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    18. Ádám Csápai, 0000. "Macroeconomic Forecasting Using Machine Learning: A Case of Slovakia," Proceedings of Economics and Finance Conferences 14115967, International Institute of Social and Economic Sciences.
    19. Seshadri Tirunillai & Gerard J. Tellis, 2017. "Does Offline TV Advertising Affect Online Chatter? Quasi-Experimental Analysis Using Synthetic Control," Marketing Science, INFORMS, vol. 36(6), pages 862-878, November.
    20. Carlos Medel, 2015. "Inflation Dynamics and the Hybrid Neo Keynesian Phillips Curve: The Case of Chile," Working Papers Central Bank of Chile 769, Central Bank of Chile.
    21. Çakmaklı, Cem & van Dijk, Dick, 2016. "Getting the most out of macroeconomic information for predicting excess stock returns," International Journal of Forecasting, Elsevier, vol. 32(3), pages 650-668.
    22. Francisco Corona & Pilar Poncela & Esther Ruiz, 2017. "Determining the number of factors after stationary univariate transformations," Empirical Economics, Springer, vol. 53(1), pages 351-372, August.
    23. Tae-Hwy Lee & Shahnaz Parsaeian & Aman Ullah, 2022. "Forecasting under Structural Breaks Using Improved Weighted Estimation," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202212, University of Kansas, Department of Economics.
    24. Masayoshi Hayashi, 2012. "Forecasting Welfare Caseloads: The Case of the Japanese Public Assistance Program," CIRJE F-Series CIRJE-F-846, CIRJE, Faculty of Economics, University of Tokyo.
    25. Stig V. Møller & Jesper Rangvid, 2018. "Global Economic Growth and Expected Returns Around the World: The End-of-the-Year Effect," Management Science, INFORMS, vol. 64(2), pages 573-591, February.
    26. Medel, Carlos A., 2015. "Forecasting Inflation with the Hybrid New Keynesian Phillips Curve: A Compact-Scale Global VAR Approach," MPRA Paper 67081, University Library of Munich, Germany.
    27. Pilar Poncela & Eva Senra, 2017. "Measuring uncertainty and assessing its predictive power in the euro area," Empirical Economics, Springer, vol. 53(1), pages 165-182, August.
    28. Karlsson, Sune, 2012. "Forecasting with Bayesian Vector Autoregressions," Working Papers 2012:12, Örebro University, School of Business.
    29. Ruijun Bu & Rodrigo Hizmeri & Marwan Izzeldin & Anthony Murphy & Mike G. Tsionas, 2021. "The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility," Working Papers 202109, University of Liverpool, Department of Economics.
    30. Katja Drechsel & Dr. Rolf Scheufele, 2012. "Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment," Working Papers 2012-16, Swiss National Bank.
    31. Romeo Ionescu, 2012. "The Economic Recovery across the EU vs the Global Crisis," EuroEconomica, Danubius University of Galati, issue 2(31), pages 30-39, May.
    32. Bialowolski, Piotr & Kuszewski, Tomasz & Witkowski, Bartosz, 2015. "Bayesian averaging vs. dynamic factor models for forecasting economic aggregates with tendency survey data," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 9, pages 1-37.
    33. 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.
    34. Georgios Moratis & Plutarchos Sakellaris, 2017. "Measuring the systemic importance of banks," Working Papers 240, Bank of Greece.
    35. W. Hölzl & S. Kaniovski & Y. Kaniovski, 2019. "Exploring the dynamics of business survey data using Markov models," Computational Management Science, Springer, vol. 16(4), pages 621-649, October.
    36. Fritz Breuss, 2016. "Would DSGE Models have Predicted the Great Recession in Austria?," WIFO Working Papers 530, WIFO.
    37. Bec, Frédérique & Mogliani, Matteo, 2015. "Nowcasting French GDP in real-time with surveys and “blocked” regressions: Combining forecasts or pooling information?," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1021-1042.
    38. Tony Chernis & Rodrigo Sekkel, 2017. "A Dynamic Factor Model for Nowcasting Canadian GDP Growth," Staff Working Papers 17-2, Bank of Canada.
    39. S. Yanki Kalfa & Jaime Marquez, 2021. "Forecasting FOMC Forecasts," Econometrics, MDPI, vol. 9(3), pages 1-21, September.
      • S. Yanki Kalfa & Jaime Marquez, 2018. "Forecasting FOMC Forecasts," Working Papers 2018-007, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    40. Zongwu Cai & Gunawan, 2023. "A Combination Forecast for Nonparametric Models with Structural Breaks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202310, University of Kansas, Department of Economics, revised Sep 2023.
    41. Franses, Philip Hans & Legerstee, Rianne, 2013. "Do statistical forecasting models for SKU-level data benefit from including past expert knowledge?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 80-87.
    42. Jones, A. & Lomas, J. & Rice, N., 2014. "Going Beyond the Mean in Healthcare Cost Regressions: a Comparison of Methods for Estimating the Full Conditional Distribution," Health, Econometrics and Data Group (HEDG) Working Papers 14/26, HEDG, c/o Department of Economics, University of York.
    43. 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.
    44. Aviral Kumar Tiwari & Rangan Gupta & Juncal Cunado & Xin Sheng, 2019. "Testing the White Noise Hypothesis in High-Frequency Housing Returns of the United States," Working Papers 201952, University of Pretoria, Department of Economics.
    45. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2012. "Common drifting volatility in large Bayesian VARs," Working Papers (Old Series) 1206, Federal Reserve Bank of Cleveland.
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    47. 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.
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    49. Jones, A & Lomas, J & Rice, N, 2011. "Applying Beta-type Size Distributions to Healthcare Cost Regressions," Health, Econometrics and Data Group (HEDG) Working Papers 11/31, HEDG, c/o Department of Economics, University of York.
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    51. Jennifer Castle & David Hendry, 2013. "Semi-automatic Non-linear Model selection," Economics Series Working Papers 654, University of Oxford, Department of Economics.
    52. Jose M. Carabias, 2018. "The real-time information content of macroeconomic news: implications for firm-level earnings expectations," Review of Accounting Studies, Springer, vol. 23(1), pages 136-166, March.
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    54. Yuanyuan Gu & Sonia García-Pérez & John Massie & Kees Gool, 2015. "Cost of care for cystic fibrosis: an investigation of cost determinants using national registry data," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(7), pages 709-717, September.
    55. Mundt, Philipp & Alfarano, Simone & Milaković, Mishael, 2019. "Exploiting ergodicity in forecasts of corporate profitability," BERG Working Paper Series 147, Bamberg University, Bamberg Economic Research Group.
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    59. Elena Andreou & Constantinos Kourouyiannis & Andros Kourtellos, 2012. "Volatility Forecast Combinations using Asymmetric Loss Functions," University of Cyprus Working Papers in Economics 07-2012, University of Cyprus Department of Economics.
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    64. Jennifer Castle & Takamitsu Kurita, 2019. "Modelling and forecasting the dollar-pound exchange rate in the presence of structural breaks," Economics Series Working Papers 866, University of Oxford, Department of Economics.
    65. Kenneth W Clements & Grace Gao, 2013. "A Multi-Market Approach to Measuring the Cycle," Economics Discussion / Working Papers 13-16, The University of Western Australia, Department of Economics.
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    72. David Hendry & Grayham E. Mizon, 2012. "Forecasting from Structural Econometric Models," Economics Series Working Papers 597, University of Oxford, Department of Economics.
    73. Hendry, David F. & Johansen, Søren, 2015. "Model Discovery And Trygve Haavelmo’S Legacy," Econometric Theory, Cambridge University Press, vol. 31(1), pages 93-114, February.
    74. Cláudia Duarte, 2014. "Autoregressive augmentation of MIDAS regressions," Working Papers w201401, Banco de Portugal, Economics and Research Department.
    75. Gebka, Bartosz & Wohar, Mark E., 2019. "Stock return distribution and predictability: Evidence from over a century of daily data on the DJIA index," International Review of Economics & Finance, Elsevier, vol. 60(C), pages 1-25.
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    77. Luciani, Matteo & Pundit, Madhavi & Ramayandi, Arief & Veronese , Giovanni, 2015. "Nowcasting Indonesia," ADB Economics Working Paper Series 471, Asian Development Bank.
    78. Aviral Kumar Tiwari & Rangan Gupta & Mark E. Wohar, 2019. "Is the Housing Market in the United States Really Weakly-Efficient?," Working Papers 201934, University of Pretoria, Department of Economics.
    79. David F. Hendry & Grayham E. Mizon, 2013. "Unpredictability in Economic Analysis, Econometric Modeling and Forecasting," Economics Papers 2013-W04, Economics Group, Nuffield College, University of Oxford.
    80. Hugh Gravelle & Mark Dusheiko & Steve Martin & Pete Smith & Nigel Rice & Jennifer Dixon, 2011. "Modelling Individual Patient Hospital Expenditure for General Practice Budgets," Working Papers 073cherp, Centre for Health Economics, University of York.
    81. David F. Hendry & Grayham E. Mizon, 2016. "Improving the teaching of econometrics," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1170096-117, December.
    82. Marczak, Martyna & Proietti, Tommaso, 2015. "Outlier Detection in Structural Time Series Models: the Indicator Saturation Approach," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 113137, Verein für Socialpolitik / German Economic Association.
    83. 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.
    84. Maurizio Bovi, 2016. "The tale of two expectations," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(6), pages 2677-2705, November.
    85. Byun, Suk Joon & Cho, Hangjun, 2013. "Forecasting carbon futures volatility using GARCH models with energy volatilities," Energy Economics, Elsevier, vol. 40(C), pages 207-221.
    86. Minxian Yang, 2017. "Effects of idiosyncratic shocks on macroeconomic time series," Empirical Economics, Springer, vol. 53(4), pages 1441-1461, December.
    87. 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.
    88. 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.
    89. Kőrösi, Gábor, 2016. "A lány továbbra is szolgál.. [Modelling and econometrics]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(6), pages 647-667.
    90. Jorge Caiado & Nuno Crato & Pilar Poncela, 2020. "A fragmented-periodogram approach for clustering big data time series," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(1), pages 117-146, March.
    91. Gianluca De Nard & Olivier Ledoit & Michael Wolf, 2018. "Factor models for portfolio selection in large dimensions: the good, the better and the ugly," ECON - Working Papers 290, Department of Economics - University of Zurich, revised Dec 2018.
    92. Clements, Michael P, 2012. "Subjective and Ex Post Forecast Uncertainty : US Inflation and Output Growth," The Warwick Economics Research Paper Series (TWERPS) 995, University of Warwick, Department of Economics.
    93. Meriküll, Jaanika & Eamets, Raul & Humal, Katrin & Espenberg, Kerly, 2012. "Power without manpower: Forecasting labour demand for Estonian energy sector," Energy Policy, Elsevier, vol. 49(C), pages 740-750.
    94. Brum, Matias & De Rosa, Mauricio, 2021. "Too little but not too late: nowcasting poverty and cash transfers’ incidence during COVID-19’s crisis," World Development, Elsevier, vol. 140(C).
    95. Jack Fosten, 2016. "Forecast evaluation with factor-augmented models," University of East Anglia School of Economics Working Paper Series 2016-05, School of Economics, University of East Anglia, Norwich, UK..
    96. Giampiero M. Gallo, 2017. "Hendry, David F. and Doornik, Jurgen A.: Empirical model discovery and theory evaluation: automatic selection methods in econometrics," Journal of Economics, Springer, vol. 120(3), pages 279-281, April.
    97. Matthew Franklin & James Lomas & Simon Walker & Tracey Young, 2019. "An Educational Review About Using Cost Data for the Purpose of Cost-Effectiveness Analysis," PharmacoEconomics, Springer, vol. 37(5), pages 631-643, May.
    98. Sriubaite, I. & Harris, A. & Jones, A.M. & Gabbe, B., 2020. "Economic Consequences of Road Traffic Injuries. Application of the Super Learner algorithm," Health, Econometrics and Data Group (HEDG) Working Papers 20/20, HEDG, c/o Department of Economics, University of York.
    99. Elie Bouri & Rangan Gupta & Clement Kweku Kyei & Rinsuna Shivambu, 2020. "Uncertainty and Daily Predictability of Housing Returns and Volatility of the United States: Evidence from a Higher-Order Nonparametric Causality-in-Quantiles Test," Working Papers 202071, University of Pretoria, Department of Economics.
    100. Reto Cueni & Bruno S. Frey, 2014. "Forecasts and Reactivity," CREMA Working Paper Series 2014-10, Center for Research in Economics, Management and the Arts (CREMA).
    101. Asger Lunde & Kasper V. Olesen, 2014. "Modeling and Forecasting the Distribution of Energy Forward Returns - Evidence from the Nordic Power Exchange," CREATES Research Papers 2013-19, Department of Economics and Business Economics, Aarhus University.
    102. Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.
    103. Till Weigt & Bernd Wilfling, 2021. "An approach to increasing forecast‐combination accuracy through VAR error modeling," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 686-699, July.
    104. Clements, Michael P. & Galvão, Ana Beatriz, 2013. "Forecasting with vector autoregressive models of data vintages: US output growth and inflation," International Journal of Forecasting, Elsevier, vol. 29(4), pages 698-714.
    105. Castle, Jennifer L. & Kurita, Takamitsu, 2021. "A dynamic econometric analysis of the dollar-pound exchange rate in an era of structural breaks and policy regime shifts," Journal of Economic Dynamics and Control, Elsevier, vol. 128(C).
    106. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Forecasting Facing Economic Shifts, Climate Change and Evolving Pandemics," Econometrics, MDPI, vol. 10(1), pages 1-21, December.
    107. Lucchetti, Riccardo & Venetis, Ioannis A., 2020. "A replication of "A quasi-maximum likelihood approach for large, approximate dynamic factor models" (Review of Economics and Statistics, 2012)," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 14, pages 1-14.
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    109. Kock, Anders Bredahl & Callot, Laurent, 2015. "Oracle inequalities for high dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 186(2), pages 325-344.
    110. Muellbauer, John, 2018. "The Future of Macroeconomics," INET Oxford Working Papers 2018-10, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    111. Clements, Michael P. & Reade, J. James, 2020. "Forecasting and forecast narratives: The Bank of England Inflation Reports," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1488-1500.
    112. Hallin, Marc & Lippi, Marco, 2013. "Factor models in high-dimensional time series—A time-domain approach," Stochastic Processes and their Applications, Elsevier, vol. 123(7), pages 2678-2695.
    113. 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.
    114. Sungchul Park & Anirban Basu, 2018. "Alternative evaluation metrics for risk adjustment methods," Health Economics, John Wiley & Sons, Ltd., vol. 27(6), pages 984-1010, June.
    115. Bhattarai, Keshab & Mallick, Sushanta K. & Yang, Bo, 2021. "Are global spillovers complementary or competitive? Need for international policy coordination," Journal of International Money and Finance, Elsevier, vol. 110(C).
    116. Michael P. Clements, 2014. "Forecast Uncertainty- Ex Ante and Ex Post : U.S. Inflation and Output Growth," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 206-216, April.
    117. Riccardo (Jack) Lucchetti & Ioannis A. Venetis, 2019. "Dynamic Factor Models in gretl. The DFM package," gretl working papers 7, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    118. Yuxuan Huang, 2016. "Forecasting the USD/CNY Exchange Rate under Different Policy Regimes," Working Papers 2016-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    119. Schanne, Norbert, 2015. "A Global Vector Autoregression (GVAR) model for regional labour markets and its forecasting performance with leading indicators in Germany," IAB-Discussion Paper 201513, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    120. María del Pilar Cruz N. & Hugo Peralta V. & Juan Pablo Cova M., 2022. "Utilización de noticias de prensa como indicador de confianza económica en tiempo real," Working Papers Central Bank of Chile 938, Central Bank of Chile.
    121. Götz, Thomas B. & Hauzenberger, Klemens, 2018. "Large mixed-frequency VARs with a parsimonious time-varying parameter structure," Discussion Papers 40/2018, Deutsche Bundesbank.
    122. Eduard Baitinger & Christian Fieberg & Thorsten Poddig & Armin Varmaz, 2015. "Liquidity-driven approach to dynamic asset allocation: evidence from the German stock market," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 29(4), pages 365-379, November.
    123. Matías Brum & Mauricio de Rosa, 2020. "Too little but not too late. Nowcasting poverty and cash transfers' incidence in Uruguay during COVID-19's crisis," Documentos de Trabajo (working papers) 20-09, Instituto de Economía - IECON.
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    127. Jongsay Yong & Ou Yang, 2021. "Does socioeconomic status affect hospital utilization and health outcomes of chronic disease patients?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(2), pages 329-339, March.
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    129. Eicher, Theo S. & Rollinson, Yuan Gao, 2023. "The accuracy of IMF crises nowcasts," International Journal of Forecasting, Elsevier, vol. 39(1), pages 431-449.

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    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. Corielli, Francesco & Marcellino, Massimiliano, 2006. "Factor based index tracking," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2215-2233, August.
    3. Håvard Hungnes, 2001. "Estimating and Restricting Growth Rates and Cointegration Means With Applications to Consumption and Money Demand," Discussion Papers 309, Statistics Norway, Research Department.
    4. Guillaume Chevillon, 2006. "Multi-step Forecasting in Unstable Economies: Robustness Issues in the Presence of Location Shifts," Economics Series Working Papers 257, University of Oxford, Department of Economics.
    5. Camille Logeay & Sven Schreiber, 2006. "Testing the effectiveness of the French work-sharing reform: a forecasting approach," Applied Economics, Taylor & Francis Journals, vol. 38(17), pages 2053-2068.
    6. Arranz, Miguel A. & Escribano, Álvaro, 1998. "Bootstraping cointegration tests under structural co-breaks: a robust extended ECM test," DES - Working Papers. Statistics and Econometrics. WS 4552, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. J James Reade & Ulrich Volz, 2010. "Too Much to Lose, or More to Gain? Should Sweden Join the Euro?," Discussion Papers 10-13, Department of Economics, University of Birmingham.
    8. Sjoerd van den Hauwe & Richard Paap & Dick J.C. van Dijk, 2011. "An Alternative Bayesian Approach to Structural Breaks in Time Series Models," Tinbergen Institute Discussion Papers 11-023/4, Tinbergen Institute.
    9. Geir E. Alstad, 2010. "The long-run exchange rate for NOK: a BEER approach," Working Paper 2010/19, Norges Bank.
    10. Sørensen, Nils Karl, 2002. "Modelling and seasonal forecasting of monthly hotel nights in Denmark," ERSA conference papers ersa02p114, European Regional Science Association.
    11. Qayyum, Abdul & Bilquees, Faiz, 2005. "P-Star Model: A Leading Indicator of Inflation for Pakistan," MPRA Paper 2058, University Library of Munich, Germany, revised 2005.
    12. D. Kuang & B. Nielsen & J. P. Nielsen, 2008. "Forecasting with the age-period-cohort model and the extended chain-ladder model," Biometrika, Biometrika Trust, vol. 95(4), pages 987-991.
    13. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    14. Zeyyad Mandalinci, 2015. "Forecasting Inflation in Emerging Markets: An Evaluation of Alternative Models," CReMFi Discussion Papers 3, CReMFi, School of Economics and Finance, QMUL.
    15. 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.
    16. Pesaran, M.H. & Pettenuzzo, D. & Timmermann, A., 2006. "Learning, Structural Instability and Present Value Calculations," Cambridge Working Papers in Economics 0602, Faculty of Economics, University of Cambridge.
    17. Garrouste, Christelle, 2011. "Towards a benchmark on the contribution of education and training to employability: methodological note," MPRA Paper 37153, University Library of Munich, Germany.
    18. Anindya Banerjee & Massimiliano Marcellino & Igor Masten, 2008. "Forecasting Macroeconomic Variables Using Diffusion Indexes in Short Samples with Structural Change," Economics Working Papers ECO2008/17, European University Institute.
    19. Duncan, Roberto & Martínez-García, Enrique, 2019. "New perspectives on forecasting inflation in emerging market economies: An empirical assessment," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1008-1031.
    20. Vitek, Francis, 2006. "Measuring the Stance of Monetary Policy in a Small Open Economy: A Dynamic Stochastic General Equilibrium Approach," MPRA Paper 802, University Library of Munich, Germany.
    21. Janine Aron & John Muellbauer, 2002. "Interest Rate Effects on Output: Evidence from a GDP Forecasting Model for South Africa," IMF Staff Papers, Palgrave Macmillan, vol. 49(Special i), pages 185-213.
    22. David Hendry & Guillaume Chevillon, 2004. "Non-Parametric Direct Multi-step Estimation for Forecasting Economic Processes," Economics Series Working Papers 196, University of Oxford, Department of Economics.
    23. 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.
    24. Pesaran, M.H. & Timmermann, A., 2003. "Small Sample Properties of Forecasts from Autoregressive Models under Structural Breaks," Cambridge Working Papers in Economics 0331, Faculty of Economics, University of Cambridge.
    25. Pinto, Jeronymo Marcondes & Marçal, Emerson Fernandes, 2019. "Cross-validation based forecasting method: a machine learning approach," Textos para discussão 498, FGV EESP - Escola de Economia de São Paulo, Fundação Getulio Vargas (Brazil).
    26. Peter C.B. Phillips, 2003. "Laws and Limits of Econometrics," Cowles Foundation Discussion Papers 1397, Cowles Foundation for Research in Economics, Yale University.
    27. David F. Hendry, 2002. "Forecast Failure, Expectations Formation and the Lucas Critique," Annals of Economics and Statistics, GENES, issue 67-68, pages 21-40.
    28. Guillaume Chevillon, 2007. "Direct Multi‐Step Estimation And Forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 746-785, September.
    29. Jeronymo Marcondes Pinto & Jennifer L. Castle, 2022. "Machine Learning Dynamic Switching Approach to Forecasting in the Presence of Structural Breaks," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 18(2), pages 129-157, July.
    30. J. M. Kargbo, 2007. "Forecasting agricultural exports and imports in South Africa," Applied Economics, Taylor & Francis Journals, vol. 39(16), pages 2069-2084.
    31. Fernald, John, 2006. "Trend Breaks, Long-Run Restrictions and the Contractionary Effects of Technology Improvements," CEPR Discussion Papers 5631, C.E.P.R. Discussion Papers.
    32. 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.
    33. da Silva Filho, Tito Nícias Teixeira, 2005. "Is there too much certainty when measuring uncertainty," MPRA Paper 16383, University Library of Munich, Germany.
    34. Søren Johansen & Rocco Mosconi & Bent Nielsen, 2000. "Cointegration analysis in the presence of structural breaks in the deterministic trend," Econometrics Journal, Royal Economic Society, vol. 3(2), pages 216-249.
    35. Neil R. Ericsson, 2000. "Predictable uncertainty in economic forecasting," International Finance Discussion Papers 695, Board of Governors of the Federal Reserve System (U.S.).
    36. 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.
    37. Davide Ciferri & Maria Chiara D’Errico & Paolo Polinori, 2020. "Integration and convergence in European electricity markets," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 37(2), pages 463-492, July.
    38. Filippo di Mauro & L. Vanessa Smith & Stephane Dees & M. Hashem Pesaran, 2007. "Exploring the international linkages of the euro area: a global VAR analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(1), pages 1-38.
    39. Jennifer Castle & David Hendry & Nicholas W.P. Fawcett, 2008. "Forecasting with Equilibrium-correction Models during Structural Breaks," Economics Series Working Papers 408, University of Oxford, Department of Economics.
    40. Gary Koop & Simon M. Potter, 2004. "Prior Elicitation in Multiple Change-point Models," Discussion Papers in Economics 04/26, Division of Economics, School of Business, University of Leicester.
    41. Gael M. Martin, 2000. "US deficit sustainability: a new approach based on multiple endogenous breaks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(1), pages 83-105.
    42. James Mitchell & Richard J. Smith & Martin R. Weale, 2002. "Quantification of Qualitative Firm-Level Survey Data," Economic Journal, Royal Economic Society, vol. 112(478), pages 117-135, March.
    43. Guglielmo Maria Caporale & Davide Ciferri & Alessandro Girardi, 2011. "Are The Baltic Countries Ready To Adopt The Euro? A Generalized Purchasing Power Parity Approach," Manchester School, University of Manchester, vol. 79(3), pages 429-454, June.
    44. Heather M. Anderson & Chin Nam Low, 2006. "Random Walk Smooth Transition Autoregressive Models," Contributions to Economic Analysis, in: Nonlinear Time Series Analysis of Business Cycles, pages 247-281, Emerald Group Publishing Limited.
    45. Alberto Baffigi & Roberto Golinelli & Giuseppe Parigi, 2002. "Real-time GDP forecasting in the euro area," Temi di discussione (Economic working papers) 456, Bank of Italy, Economic Research and International Relations Area.
    46. Eitrheim,O. & Jansen,E.S. & Nymoen,R., 2000. "Progress from forecast failure : the Norwegian consumption function," Memorandum 32/2000, Oslo University, Department of Economics.
    47. Massimiliano Marcellino, "undated". "Instability and non-linearity in the EMU," Working Papers 211, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    48. Neil R. Ericsson, 2001. "Forecast uncertainty in economic modeling," International Finance Discussion Papers 697, Board of Governors of the Federal Reserve System (U.S.).
    49. Daniele Antonucci & Alessandro Girardi, 2005. "Structural changes and deviations from the PPP within the Euro Area," ISAE Working Papers 57, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
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