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Davide Delle Monache

Personal Details

First Name:Davide
Middle Name:
Last Name:Delle Monache
Suffix:
RePEc Short-ID:pde480
[This author has chosen not to make the email address public]
https://sites.google.com/site/dellemonachedavide/home
Via Nazionale, 91. 00184. Rome. italy
Terminal Degree:2011 (from RePEc Genealogy)

Affiliation

Banca d'Italia

Roma, Italy
http://www.bancaditalia.it/
RePEc:edi:bdigvit (more details at EDIRC)

Research output

as
Jump to: Working papers Articles Chapters

Working papers

  1. Stefano Neri & Fabio Busetti & Cristina Conflitti & Francesco Corsello & Davide Delle Monache & Alex Tagliabracci, 2023. "Energy price shocks and inflation in the euro area," Questioni di Economia e Finanza (Occasional Papers) 792, Bank of Italy, Economic Research and International Relations Area.
  2. Delle Monache, Davide & De Polis, Andrea & Petrella, Ivan, 2021. "Modeling and forecasting macroeconomic downside risk," Temi di discussione (Economic working papers) 1324, Bank of Italy, Economic Research and International Relations Area.
  3. Fabio Busetti & Michele Caivano & Davide Delle Monache & Claudia Pacella, 2020. "The time-varying risk of Italian GDP," Temi di discussione (Economic working papers) 1288, Bank of Italy, Economic Research and International Relations Area.
  4. Delle Monache, Davide & Petrella, Ivan & Venditti, Fabrizio, 2020. "Price dividend ratio and long-run stock returns: a score driven state space model," Temi di discussione (Economic working papers) 1296, Bank of Italy, Economic Research and International Relations Area.
  5. Fabio Busetti & Michele Caivano & Davide Delle Monache, 2019. "Domestic and global determinants of inflation: evidence from expectile regression," Temi di discussione (Economic working papers) 1225, Bank of Italy, Economic Research and International Relations Area.
  6. Delle Monache, Davide & Petrella, Ivan, 2019. "Efficient Matrix Approach for Classical Inference in State Space Models," EMF Research Papers 19, Economic Modelling and Forecasting Group.
  7. Guido Bulligan & Davide Delle Monache, 2018. "Financial markets effects of ECB unconventional monetary policy announcements," Questioni di Economia e Finanza (Occasional Papers) 424, Bank of Italy, Economic Research and International Relations Area.
  8. Davide Delle Monache & Stefano Grassi & Paolo Santucci de Magistris, 2017. "Does the ARFIMA really shift?," CREATES Research Papers 2017-16, Department of Economics and Business Economics, Aarhus University.
  9. Locarno, Alberto & Delle Monache, Davide & Busetti, Fabio & Gerali, Andrea, 2017. "Trust, but verify. De-anchoring of inflation expectations under learning and heterogeneity," Working Paper Series 1994, European Central Bank.
  10. Guido Bulligan & Lorenzo Burlon & Davide Delle Monache & Andrea Silvestrini, 2017. "Real and financial cycles: estimates using unobserved component models for the Italian economy," Questioni di Economia e Finanza (Occasional Papers) 382, Bank of Italy, Economic Research and International Relations Area.
  11. Davide Delle Monache & Ivan Petrella, 2016. "Adaptive models and heavy tails with an application to inflation forecasting," BCAM Working Papers 1603, Birkbeck Centre for Applied Macroeconomics.
  12. Petrella, Ivan & Venditti, Fabrizio & Delle Monache, Davide, 2016. "Adaptive state space models with applications to the business cycle and financial stress," CEPR Discussion Papers 11599, C.E.P.R. Discussion Papers.
  13. Davide Delle Monache & Stefano Grassi & Paolo Santucci de Magistris, 2015. "Testing for Level Shifts in Fractionally Integrated Processes: a State Space Approach," CREATES Research Papers 2015-30, Department of Economics and Business Economics, Aarhus University.
  14. Delle Monache & Ivan Petrella & Fabrizio Venditti, 2015. "Common faith or parting ways? A time varying parameters factor analysis of euro-area inflation," Birkbeck Working Papers in Economics and Finance 1515, Birkbeck, Department of Economics, Mathematics & Statistics.
  15. Davide Delle Monache & Ivan Petrella, 2014. "Adaptive Models and Heavy Tails," Birkbeck Working Papers in Economics and Finance 1409, Birkbeck, Department of Economics, Mathematics & Statistics.

    repec:qmw:qmwecw:wp720 is not listed on IDEAS

Articles

  1. Fabio Busetti & Michele Caivano & Davide Delle Monache, 2021. "Domestic and Global Determinants of Inflation: Evidence from Expectile Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(4), pages 982-1001, August.
  2. Davide Delle Monache & Ivan Petrella & Fabrizio Venditti, 2021. "Price Dividend Ratio and Long-Run Stock Returns: A Score-Driven State Space Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 1054-1065, October.
  3. Busetti, Fabio & Caivano, Michele & Delle Monache, Davide & Pacella, Claudia, 2021. "The time-varying risk of Italian GDP," Economic Modelling, Elsevier, vol. 101(C).
  4. Delle Monache, Davide & Petrella, Ivan, 2019. "Efficient matrix approach for classical inference in state space models," Economics Letters, Elsevier, vol. 181(C), pages 22-27.
  5. Guido Bulligan & Lorenzo Burlon & Davide Delle Monache & Andrea Silvestrini, 2019. "Real and financial cycles: estimates using unobserved component models for the Italian economy," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 541-569, September.
  6. Delle Monache, Davide & Petrella, Ivan, 2017. "Adaptive models and heavy tails with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 33(2), pages 482-501.
  7. Harvey, Andrew C. & Delle Monache, Davide, 2009. "Computing the mean square error of unobserved components extracted by misspecified time series models," Journal of Economic Dynamics and Control, Elsevier, vol. 33(2), pages 283-295, February.
  8. Mario Mazzocchi & Davide Delle Monache & Alexandra Lobb, 2006. "A structural time series approach to modelling multiple and resurgent meat scares in Italy," Applied Economics, Taylor & Francis Journals, vol. 38(14), pages 1677-1688.

Chapters

  1. Davide Delle Monache & Ivan Petrella & Fabrizio Venditti, 2016. "Common Faith or Parting Ways? A Time Varying Parameters Factor Analysis of Euro-Area Inflation," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 539-565, Emerald Group Publishing Limited.

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.

Working papers

  1. Stefano Neri & Fabio Busetti & Cristina Conflitti & Francesco Corsello & Davide Delle Monache & Alex Tagliabracci, 2023. "Energy price shocks and inflation in the euro area," Questioni di Economia e Finanza (Occasional Papers) 792, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Adolfsen, Jakob Feveile & Ferrari Minesso, Massimo & Mork, Jente Esther & Van Robays, Ine, 2024. "Gas price shocks and euro area inflation," Working Paper Series 2905, European Central Bank.

  2. Delle Monache, Davide & De Polis, Andrea & Petrella, Ivan, 2021. "Modeling and forecasting macroeconomic downside risk," Temi di discussione (Economic working papers) 1324, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Chen, Guojin & Liu, Yanzhen & Zhang, Yu, 2020. "Can systemic risk measures predict economic shocks? Evidence from China," China Economic Review, Elsevier, vol. 64(C).
    2. Aaron J. Amburgey & Michael W. McCracken, 2023. "On the real‐time predictive content of financial condition indices for growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 137-163, March.
    3. Paul Labonne & Leif Anders Thorsrud, 2023. "Risky news and credit market sentiment," Working Papers No 14/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    4. Lloyd, S. & Manuel, E. & Panchev, K., 2021. "Foreign Vulnerabilities, Domestic Risks: The Global Drivers of GDP-at-Risk," Cambridge Working Papers in Economics 2156, Faculty of Economics, University of Cambridge.
    5. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    6. Patrick A. Adams & Tobias Adrian & Nina Boyarchenko & Domenico Giannone, 2020. "Forecasting Macroeconomic Risks," Staff Reports 914, Federal Reserve Bank of New York.
    7. Giacomo Bormetti & Fulvio Corsi, 2021. "A Lucas Critique Compliant SVAR model with Observation-driven Time-varying Parameters," Papers 2107.05263, arXiv.org, revised Feb 2022.
    8. James Mitchell & Aubrey Poon & Dan Zhu, 2022. "Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics," Working Papers 22-12R, Federal Reserve Bank of Cleveland, revised 11 Apr 2023.
    9. Lhuissier Stéphane, 2022. "Financial Conditions and Macroeconomic Downside Risks in the Euro Area," Working papers 863, Banque de France.
    10. Gloria Gonzalez-Rivera & Vladimir Rodriguez-Caballero & Esther Ruiz, 2021. "Expecting the unexpected: economic growth under stress," Working Papers 202106, University of California at Riverside, Department of Economics.
    11. Leopoldo Catania & Alessandra Luati & Pierluigi Vallarino, 2021. "Economic vulnerability is state dependent," CREATES Research Papers 2021-09, Department of Economics and Business Economics, Aarhus University.
    12. Sune Karlsson & Stepan Mazur & Hoang Nguyen, 2021. "Vector autoregression models with skewness and heavy tails," Papers 2105.11182, arXiv.org.
    13. Wolf, Elias, 2022. "Estimating growth at risk with skewed stochastic volatility models," Discussion Papers 2022/2, Free University Berlin, School of Business & Economics.
    14. Jan Pruser & Florian Huber, 2023. "Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions," Papers 2301.13604, arXiv.org, revised Sep 2023.
    15. 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.
    16. Iseringhausen, Martin, 2024. "A time-varying skewness model for Growth-at-Risk," International Journal of Forecasting, Elsevier, vol. 40(1), pages 229-246.
    17. Marcellino, Massimiliano & Clark, Todd & Huber, Florian & Koop, Gary & Pfarrhofer, Michael, 2022. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," CEPR Discussion Papers 17461, C.E.P.R. Discussion Papers.
    18. Botelho, Vasco & Foroni, Claudia & Renzetti, Andrea, 2023. "Labour at risk," Working Paper Series 2840, European Central Bank.
    19. Fabio Busetti & Michele Caivano & Davide Delle Monache & Claudia Pacella, 2020. "The time-varying risk of Italian GDP," Temi di discussione (Economic working papers) 1288, Bank of Italy, Economic Research and International Relations Area.
    20. Stolbov, Mikhail & Shchepeleva, Maria, 2022. "Modeling global real economic activity: Evidence from variable selection across quantiles," The Journal of Economic Asymmetries, Elsevier, vol. 25(C).
    21. Paul Labonne, 2022. "Asymmetric Uncertainty: Nowcasting Using Skewness in Real-time Data," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-23, Economic Statistics Centre of Excellence (ESCoE).
    22. Helena Chuliá & Ignacio Garrón & Jorge M. Uribe, 2021. ""Vulnerable Funding in the Global Economy"," IREA Working Papers 202106, University of Barcelona, Research Institute of Applied Economics, revised Mar 2021.
    23. Deng, Chuang & Wu, Jian, 2023. "Macroeconomic downside risk and the effect of monetary policy," Finance Research Letters, Elsevier, vol. 54(C).
    24. Boriss Siliverstovs, 2021. "Gauging the Effect of Influential Observations on Measures of Relative Forecast Accuracy in a Post-COVID-19 Era: Application to Nowcasting Euro Area GDP Growth," Working Papers 2021/01, Latvijas Banka.
    25. Pacelli, Vincenzo & Miglietta, Federica & Foglia, Matteo, 2022. "The extreme risk connectedness of the new financial system: European evidence," International Review of Financial Analysis, Elsevier, vol. 84(C).
    26. Paul Labonne, 2020. "Capturing GDP nowcast uncertainty in real time," Papers 2012.02601, arXiv.org, revised Oct 2021.
    27. Michal Franta & Jan Libich, 2021. "Holding the Economy by the Tail: Analysis of Short- and Long-run Macroeconomic Risks," Working Papers 2021/3, Czech National Bank.

  3. Fabio Busetti & Michele Caivano & Davide Delle Monache & Claudia Pacella, 2020. "The time-varying risk of Italian GDP," Temi di discussione (Economic working papers) 1288, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Gloria Gonzalez-Rivera & Vladimir Rodriguez-Caballero & Esther Ruiz, 2021. "Expecting the unexpected: economic growth under stress," Working Papers 202106, University of California at Riverside, Department of Economics.
    2. J. David López-Salido & Francesca Loria, 2020. "Inflation at Risk," Finance and Economics Discussion Series 2020-013, Board of Governors of the Federal Reserve System (U.S.).
    3. Gu, Xin & Cheng, Xiang & Zhu, Zixiang & Deng, Xiang, 2021. "Economic policy uncertainty and China’s growth-at-risk," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 452-467.
    4. Anastasiya Ivanova & Alona Shmygel & Ihor Lubchuk, 2021. "The Growth-at-Risk (GaR) Framework: Implication For Ukraine," IHEID Working Papers 10-2021, Economics Section, The Graduate Institute of International Studies.

  4. Delle Monache, Davide & Petrella, Ivan & Venditti, Fabrizio, 2020. "Price dividend ratio and long-run stock returns: a score driven state space model," Temi di discussione (Economic working papers) 1296, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Eric A. Beutner & Yicong Lin & Andre Lucas, 2023. "Consistency, distributional convergence, and optimality of score-driven filters," Tinbergen Institute Discussion Papers 23-051/III, Tinbergen Institute.

  5. Fabio Busetti & Michele Caivano & Davide Delle Monache, 2019. "Domestic and global determinants of inflation: evidence from expectile regression," Temi di discussione (Economic working papers) 1225, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Brand, Claus & Obstbaum, Meri & Coenen, Günter & Sondermann, David & Lydon, Reamonn & Ajevskis, Viktors & Hammermann, Felix & Angino, Siria & Hernborg, Nils & Basso, Henrique & Hertweck, Matthias & Bi, 2021. "Employment and the conduct of monetary policy in the euro area," Occasional Paper Series 275, European Central Bank.
    2. Jana Budova & Veronika Sulikova & Marianna Sinicakova, 2023. "When Inflation Again Matters: Do Domestic and Global Output Gaps Determine Inflation in the EU?," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 25(63), pages 575-575, April.
    3. Fabio Busetti & Michele Caivano & Davide Delle Monache & Claudia Pacella, 2020. "The time-varying risk of Italian GDP," Temi di discussione (Economic working papers) 1288, Bank of Italy, Economic Research and International Relations Area.
    4. Stefano Neri & Fabio Busetti & Cristina Conflitti & Francesco Corsello & Davide Delle Monache & Alex Tagliabracci, 2023. "Energy price shocks and inflation in the euro area," Questioni di Economia e Finanza (Occasional Papers) 792, Bank of Italy, Economic Research and International Relations Area.
    5. Philipp F. M. Baumann & Enzo Rossi & Alexander Volkmann, 2020. "What Drives Inflation and How: Evidence from Additive Mixed Models Selected by cAIC," Papers 2006.06274, arXiv.org, revised Aug 2022.

  6. Delle Monache, Davide & Petrella, Ivan, 2019. "Efficient Matrix Approach for Classical Inference in State Space Models," EMF Research Papers 19, Economic Modelling and Forecasting Group.

    Cited by:

    1. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
    2. Poncela, Pilar & Ruiz, Esther & Miranda, Karen, 2021. "Factor extraction using Kalman filter and smoothing: This is not just another survey," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1399-1425.
    3. Zakipour-Saber, Shayan, 2019. "State-dependent Monetary Policy Regimes," Research Technical Papers 4/RT/19, Central Bank of Ireland.
    4. Matteo Barigozzi, 2023. "Quasi Maximum Likelihood Estimation of High-Dimensional Factor Models: A Critical Review," Papers 2303.11777, arXiv.org, revised Dec 2023.

  7. Guido Bulligan & Davide Delle Monache, 2018. "Financial markets effects of ECB unconventional monetary policy announcements," Questioni di Economia e Finanza (Occasional Papers) 424, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Havlik, Annika & Heinemann, Friedrich & Helbig, Samuel & Nover, Justus, 2021. "Dispelling the shadow of fiscal dominance? Fiscal and monetary announcement effects for euro area sovereign spreads in the corona pandemic," ZEW Discussion Papers 21-050, ZEW - Leibniz Centre for European Economic Research.
    2. Caporin, Massimiliano & Pelizzon, Loriana & Plazzi, Alberto, 2020. "Does monetary policy impact international market co-movements?," SAFE Working Paper Series 276, Leibniz Institute for Financial Research SAFE.
    3. Siekmann, Helmut & Wieland, Volker, 2020. "The ruling of the Federal Constitutional Court concerning the public sector purchase program: A practical way forward," IMFS Working Paper Series 140, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    4. Stefano Neri & Stefano Siviero, 2019. "The non-standard monetary policy measures of the ECB: motivations, effectiveness and risks," Questioni di Economia e Finanza (Occasional Papers) 486, Bank of Italy, Economic Research and International Relations Area.
    5. Martina Cecioni, 2018. "ECB monetary policy and the euro exchange rate," Temi di discussione (Economic working papers) 1172, Bank of Italy, Economic Research and International Relations Area.
    6. Marco Bottone & Alfonso Rosolia, 2019. "Monetary policy, firms’ inflation expectations and prices: causal evidence from firm-level data," Temi di discussione (Economic working papers) 1218, Bank of Italy, Economic Research and International Relations Area.

  8. Locarno, Alberto & Delle Monache, Davide & Busetti, Fabio & Gerali, Andrea, 2017. "Trust, but verify. De-anchoring of inflation expectations under learning and heterogeneity," Working Paper Series 1994, European Central Bank.

    Cited by:

    1. Christian Dreger, 2017. "Long Term Growth Perspectives in Japan and the Euro Area," Discussion Papers of DIW Berlin 1661, DIW Berlin, German Institute for Economic Research.
    2. Eser, Fabian & Karadi, Peter & Lane, Philip R. & Moretti, Laura & Osbat, Chiara, 2020. "The Phillips Curve at the ECB," Working Paper Series 2400, European Central Bank.
    3. Gabriele Galati & Richhild Moessner & Maarten van Rooij, 2020. "The anchoring of long-term inflation expectations of consumers: insights from a new survey," Working Papers 688, DNB.
    4. Giuliana Passamani & Alessandro Sardone & Roberto Tamborini, 2020. "Phillips Curve and output expectations: New perspectives from the Euro Zone," DEM Working Papers 2020/6, Department of Economics and Management.
    5. Stefano Neri & Stefano Siviero, 2019. "The non-standard monetary policy measures of the ECB: motivations, effectiveness and risks," Questioni di Economia e Finanza (Occasional Papers) 486, Bank of Italy, Economic Research and International Relations Area.
    6. Ciccarelli, Matteo & Osbat, Chiara, 2017. "Low inflation in the euro area: Causes and consequences," Occasional Paper Series 181, European Central Bank.
    7. Armantier, Olivier & Sbordone, Argia & Topa, Giorgio & van der Klaauw, Wilbert & Williams, John C., 2022. "A new approach to assess inflation expectations anchoring using strategic surveys," Journal of Monetary Economics, Elsevier, vol. 129(S), pages 82-101.
    8. Henckel, Timo & Menzies, Gordon D. & Moffatt, Peter & Zizzo, Daniel J., 2019. "Three dimensions of central bank credibility and inferential expectations: The Euro zone," Journal of Macroeconomics, Elsevier, vol. 60(C), pages 294-308.
    9. 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.
    10. Giuliana Passamani & Alessandro Sardone & Roberto Tamborini, 2022. "Inflation puzzles, the Phillips Curve and output expectations: new perspectives from the Euro Zone," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 49(1), pages 123-153, February.
    11. Goy, Gavin & Hommes, Cars & Mavromatis, Kostas, 2022. "Forward guidance and the role of central bank credibility under heterogeneous beliefs," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 1240-1274.
    12. Stefano Neri & Guido Bulligan & Sara Cecchetti & Francesco Corsello & Andrea Papetti & Marianna Riggi & Concetta Rondinelli & Alex Tagliabracci, 2022. "On the anchoring of inflation expectations in the euro area," Questioni di Economia e Finanza (Occasional Papers) 712, Bank of Italy, Economic Research and International Relations Area.
    13. Mr. Yasser Abdih & Ms. Li Lin & Anne-Charlotte Paret, 2018. "Understanding Euro Area Inflation Dynamics: Why So Low for So Long?," IMF Working Papers 2018/188, International Monetary Fund.
    14. Kevin J. Lansing, 2019. "Endogenous Forecast Switching Near the Zero Lower Bound," Working Paper Series 2017-24, Federal Reserve Bank of San Francisco.
    15. Laura Bartiloro & Marco Bottone & Alfonso Rosolia, 2017. "What does the heterogeneity of the inflation expectations of Italian firms tell us?," Questioni di Economia e Finanza (Occasional Papers) 414, Bank of Italy, Economic Research and International Relations Area.
    16. Cecchetti, Stephen & Feroli, Michael & Hooper, Peter & Kashyap, Anil & Schoenholtz, Kermit L., 2017. "Deflating Inflation Expectations: The Implications of Inflation’s Simple Dynamics," CEPR Discussion Papers 11925, C.E.P.R. Discussion Papers.
    17. Ignazio Visco & Giordano Zevi, 2020. "Bounded rationality and expectations in economics," Questioni di Economia e Finanza (Occasional Papers) 575, Bank of Italy, Economic Research and International Relations Area.

  9. Guido Bulligan & Lorenzo Burlon & Davide Delle Monache & Andrea Silvestrini, 2017. "Real and financial cycles: estimates using unobserved component models for the Italian economy," Questioni di Economia e Finanza (Occasional Papers) 382, Bank of Italy, Economic Research and International Relations Area.

    Cited by:

    1. Valentina Aprigliano & Danilo Liberati, 2019. "Using credit variables to date business cycle and to estimate the probabilities of recession in real time," Temi di discussione (Economic working papers) 1229, Bank of Italy, Economic Research and International Relations Area.
    2. Chafik, Omar & Achour, Aya, 2022. "Cycle financier, cycle réel et transmission de la politique monétaire au Maroc," Document de travail 2022-2, Bank Al-Maghrib, Département de la Recherche.
    3. Bartoletto, Silvana & Chiarini, Bruno & Marzano, Elisabetta & Piselli, Paolo, 2019. "Business cycles, credit cycles, and asymmetric effects of credit fluctuations: Evidence from Italy for the period of 1861–2013," Journal of Macroeconomics, Elsevier, vol. 61(C), pages 1-1.
    4. Filippo Gusella & Engelbert Stockhammer, 2020. "Testing fundamentalist-momentum trader financial cycles. An empirical analysis via the Kalman filter," Working Papers PKWP2009, Post Keynesian Economics Society (PKES).
    5. Filippo Gusella, 2022. "Detecting And Measuring Financial Cycles In Heterogeneous Agents Models: An Empirical Analysis," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 25(02n03), pages 1-22, March.
    6. Jasper de Winter & Siem Jan Koopman & Irma Hindrayanto, 2022. "Joint Decomposition of Business and Financial Cycles: Evidence from Eight Advanced Economies," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(1), pages 57-79, February.
    7. Roberta Fiori & Claudia Pacella, 2019. "Should the CCYB be enhanced with a sectoral dimension? The case of Italy," Questioni di Economia e Finanza (Occasional Papers) 499, Bank of Italy, Economic Research and International Relations Area.
    8. Lenarčič, Črt, 2021. "Estimating business and financial cycles in Slovenia," MPRA Paper 109977, University Library of Munich, Germany.
    9. Bogdan Andrei Dumitrescu & Robert-Adrian Grecu, 2023. "Impact of Financial Factors on the Economic Cycle Dynamics in Selected European Countries," JRFM, MDPI, vol. 16(12), pages 1-17, November.
    10. Gyurkovics, Éva & Takács, Tibor, 2022. "Robust energy-to-peak filter design for a class of unstable polytopic systems with a macroeconomic application," Applied Mathematics and Computation, Elsevier, vol. 420(C).

  10. Davide Delle Monache & Ivan Petrella, 2016. "Adaptive models and heavy tails with an application to inflation forecasting," BCAM Working Papers 1603, Birkbeck Centre for Applied Macroeconomics.

    Cited by:

    1. Paul Labonne & Leif Anders Thorsrud, 2023. "Risky news and credit market sentiment," Working Papers No 14/2023, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    2. Petrella, Ivan & Delle Monache, Davide & Venditti, Fabrizio, 2019. "Price Dividend Ratio and Long-Run Stock Returns: a Score Driven State Space Model," CEPR Discussion Papers 14107, C.E.P.R. Discussion Papers.
    3. Giacomo Bormetti & Fulvio Corsi, 2021. "A Lucas Critique Compliant SVAR model with Observation-driven Time-varying Parameters," Papers 2107.05263, arXiv.org, revised Feb 2022.
    4. Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
    5. Mogens Fosgerau & Jinwon Kim & Abhishek Ranjan, 2017. "Vickrey Meets Alonso: Commute Scheduling and Congestion in a Monocentric City," Discussion Papers 17-25, University of Copenhagen. Department of Economics.
    6. Luisa Bisaglia & Matteo Grigoletto, 2021. "A new time-varying model for forecasting long-memory series," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 139-155, March.
    7. Blasques, F. & Gorgi, P. & Koopman, S.J., 2019. "Accelerating score-driven time series models," Journal of Econometrics, Elsevier, vol. 212(2), pages 359-376.
    8. Gorgi, Paolo & Koopman, Siem Jan & Li, Mengheng, 2019. "Forecasting economic time series using score-driven dynamic models with mixed-data sampling," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1735-1747.
    9. Chen, Ji & Yang, Xinglin & Liu, Xiliang, 2022. "Learning, disagreement and inflation forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    10. Harvey, A., 2021. "Score-driven time series models," Cambridge Working Papers in Economics 2133, Faculty of Economics, University of Cambridge.
    11. Carlos Henrique Dias Cordeiro de Castro & Fernando Antonio Lucena Aiube, 2023. "Forecasting inflation time series using score‐driven dynamic models and combination methods: The case of Brazil," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 369-401, March.
    12. Delle Monache, Davide & De Polis, Andrea & Petrella, Ivan, 2021. "Modeling and forecasting macroeconomic downside risk," Temi di discussione (Economic working papers) 1324, Bank of Italy, Economic Research and International Relations Area.
    13. 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.
    14. Paul Labonne, 2022. "Asymmetric Uncertainty: Nowcasting Using Skewness in Real-time Data," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-23, Economic Statistics Centre of Excellence (ESCoE).
    15. Tretyakov, Dmitriy & Fokin, Nikita, 2020. "Помогают Ли Высокочастотные Данные В Прогнозировании Российской Инфляции? [Does the high-frequency data is helpful for forecasting Russian inflation?]," MPRA Paper 109556, University Library of Munich, Germany.
    16. Martin Weale & Paul Labonne, 2022. "Nowcasting in the presence of large measurement errors and revisions," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2022-05, Economic Statistics Centre of Excellence (ESCoE).
    17. Roberto Duncan & Enrique Martínez‐García, 2023. "Forecasting inflation in open economies: What can a NOEM model do?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 481-513, April.
    18. Fuentes, Fernanda & Herrera, Rodrigo & Clements, Adam, 2023. "Forecasting extreme financial risk: A score-driven approach," International Journal of Forecasting, Elsevier, vol. 39(2), pages 720-735.
    19. Paul Labonne, 2020. "Capturing GDP nowcast uncertainty in real time," Papers 2012.02601, arXiv.org, revised Oct 2021.

  11. Petrella, Ivan & Venditti, Fabrizio & Delle Monache, Davide, 2016. "Adaptive state space models with applications to the business cycle and financial stress," CEPR Discussion Papers 11599, C.E.P.R. Discussion Papers.

    Cited by:

    1. Giacomo Bormetti & Fulvio Corsi, 2021. "A Lucas Critique Compliant SVAR model with Observation-driven Time-varying Parameters," Papers 2107.05263, arXiv.org, revised Feb 2022.
    2. George Kapetanios & Massimiliano Marcellino & Fabrizio Venditti, 2019. "Large time‐varying parameter VARs: A nonparametric approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1027-1049, November.
    3. Angelini, Giovanni & Gorgi, Paolo, 2018. "DSGE Models with observation-driven time-varying volatility," Economics Letters, Elsevier, vol. 171(C), pages 169-171.
    4. Blasques, F. & Gorgi, P. & Koopman, S.J., 2021. "Missing observations in observation-driven time series models," Journal of Econometrics, Elsevier, vol. 221(2), pages 542-568.
    5. Simone Auer, 2017. "A Financial Conditions Index for the CEE economies," Temi di discussione (Economic working papers) 1145, Bank of Italy, Economic Research and International Relations Area.
    6. Bahcivan, Hulusi & Karahan, Cenk C., 2022. "High frequency correlation dynamics and day-of-the-week effect: A score-driven approach in an emerging market stock exchange," International Review of Financial Analysis, Elsevier, vol. 80(C).
    7. Giovanni Angelini & Paolo Gorgi, 2018. "DSGE Models with Observation-Driven Time-Varying parameters," Tinbergen Institute Discussion Papers 18-030/III, Tinbergen Institute.
    8. Caterina Schiavoni & Siem Jan Koopman & Franz Palm & Stephan Smeekes & Jan van den Brakel, 2021. "Time-varying state correlations in state space models and their estimation via indirect inference," Tinbergen Institute Discussion Papers 21-020/III, Tinbergen Institute.
    9. Giuseppe Buccheri & Giacomo Bormetti & Fulvio Corsi & Fabrizio Lillo, 2018. "A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: an Application to High-Frequency Covariance Dynamics," Papers 1803.04894, arXiv.org, revised Mar 2019.

  12. Delle Monache & Ivan Petrella & Fabrizio Venditti, 2015. "Common faith or parting ways? A time varying parameters factor analysis of euro-area inflation," Birkbeck Working Papers in Economics and Finance 1515, Birkbeck, Department of Economics, Mathematics & Statistics.

    Cited by:

    1. 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.
    2. Gary Koop & Dimitris Korobilis, 2018. "Forecasting with High-Dimensional Panel VARs," Working Paper series 18-20, Rimini Centre for Economic Analysis.
    3. Giacomo Bormetti & Fulvio Corsi, 2021. "A Lucas Critique Compliant SVAR model with Observation-driven Time-varying Parameters," Papers 2107.05263, arXiv.org, revised Feb 2022.
    4. Lodge, David & Pérez, Javier J. & Albrizio, Silvia & Everett, Mary & De Bandt, Olivier & Georgiadis, Georgios & Ca' Zorzi, Michele & Lastauskas, Povilas & Carluccio, Juan & Parrága, Susana & Carvalho,, 2021. "The implications of globalisation for the ECB monetary policy strategy," Occasional Paper Series 263, European Central Bank.
    5. Marcellino, Massimiliano & Carriero, Andrea & Corsello, Francesco, 2019. "The Global Component of Inflation Volatility," CEPR Discussion Papers 13470, C.E.P.R. Discussion Papers.
    6. Stefano Neri & Stefano Siviero, 2019. "The non-standard monetary policy measures of the ECB: motivations, effectiveness and risks," Questioni di Economia e Finanza (Occasional Papers) 486, Bank of Italy, Economic Research and International Relations Area.
    7. Fabio Busetti & Michele Caivano & Davide Delle Monache, 2019. "Domestic and global determinants of inflation: evidence from expectile regression," Temi di discussione (Economic working papers) 1225, Bank of Italy, Economic Research and International Relations Area.
    8. Giuseppe Buccheri & Giacomo Bormetti & Fulvio Corsi & Fabrizio Lillo, 2018. "A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: an Application to High-Frequency Covariance Dynamics," Papers 1803.04894, arXiv.org, revised Mar 2019.

  13. Davide Delle Monache & Ivan Petrella, 2014. "Adaptive Models and Heavy Tails," Birkbeck Working Papers in Economics and Finance 1409, Birkbeck, Department of Economics, Mathematics & Statistics.

    Cited by:

    1. Dave, Chetan & Malik, Samreen, 2017. "A tale of fat tails," European Economic Review, Elsevier, vol. 100(C), pages 293-317.
    2. F Blasques & P Gorgi & S Koopman & O Wintenberger, 2016. "Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models," Papers 1610.02863, arXiv.org.
    3. Petrella, Ivan & Venditti, Fabrizio & Delle Monache, Davide, 2016. "Adaptive state space models with applications to the business cycle and financial stress," CEPR Discussion Papers 11599, C.E.P.R. Discussion Papers.
    4. Mogens Fosgerau & Jinwon Kim & Abhishek Ranjan, 2017. "Vickrey Meets Alonso: Commute Scheduling and Congestion in a Monocentric City," Discussion Papers 17-25, University of Copenhagen. Department of Economics.
    5. George Kapetanios & Massimiliano Marcellino & Fabrizio Venditti, 2019. "Large time‐varying parameter VARs: A nonparametric approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(7), pages 1027-1049, November.
    6. Stefano Grassi & Paolo Santucci de Magistris, 2013. "It’s all about volatility (of volatility): evidence from a two-factor stochastic volatility model," CREATES Research Papers 2013-03, Department of Economics and Business Economics, Aarhus University.
    7. Mauro Bernardi & Leopoldo Catania, 2016. "Comparison of Value-at-Risk models using the MCS approach," Computational Statistics, Springer, vol. 31(2), pages 579-608, June.
    8. 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.
    9. Francisco Blasques & Paolo Gorgi & Siem Jan Koopman & Olivier Wintenberger, 2016. "Feasible Invertibility Conditions and Maximum Likelihood Estimation for Observation-Driven Models," Tinbergen Institute Discussion Papers 16-082/III, Tinbergen Institute.
    10. Francisco Blasques & Siem Jan Koopman & André Lucas, 2014. "Optimal Formulations for Nonlinear Autoregressive Processes," Tinbergen Institute Discussion Papers 14-103/III, Tinbergen Institute.

Articles

  1. Fabio Busetti & Michele Caivano & Davide Delle Monache, 2021. "Domestic and Global Determinants of Inflation: Evidence from Expectile Regression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(4), pages 982-1001, August.
    See citations under working paper version above.
  2. Davide Delle Monache & Ivan Petrella & Fabrizio Venditti, 2021. "Price Dividend Ratio and Long-Run Stock Returns: A Score-Driven State Space Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(4), pages 1054-1065, October.
    See citations under working paper version above.
  3. Busetti, Fabio & Caivano, Michele & Delle Monache, Davide & Pacella, Claudia, 2021. "The time-varying risk of Italian GDP," Economic Modelling, Elsevier, vol. 101(C).
    See citations under working paper version above.
  4. Delle Monache, Davide & Petrella, Ivan, 2019. "Efficient matrix approach for classical inference in state space models," Economics Letters, Elsevier, vol. 181(C), pages 22-27.
    See citations under working paper version above.
  5. Guido Bulligan & Lorenzo Burlon & Davide Delle Monache & Andrea Silvestrini, 2019. "Real and financial cycles: estimates using unobserved component models for the Italian economy," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 541-569, September.
    See citations under working paper version above.
  6. Delle Monache, Davide & Petrella, Ivan, 2017. "Adaptive models and heavy tails with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 33(2), pages 482-501.
    See citations under working paper version above.
  7. Harvey, Andrew C. & Delle Monache, Davide, 2009. "Computing the mean square error of unobserved components extracted by misspecified time series models," Journal of Economic Dynamics and Control, Elsevier, vol. 33(2), pages 283-295, February.

    Cited by:

    1. Flaig Gebhard, 2015. "Why We Should Use High Values for the Smoothing Parameter of the Hodrick-Prescott Filter," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 235(6), pages 518-538, December.
    2. Rodríguez, Alejandro & Ruiz Ortega, Esther, 2010. "Bootstrap prediction mean squared errors of unobserved states based on the Kalman filter with estimated parameters," DES - Working Papers. Statistics and Econometrics. WS ws100301, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Kristian Jönsson, 2017. "Restricted Hodrick–Prescott filtering in a state-space framework," Empirical Economics, Springer, vol. 53(3), pages 1243-1251, November.
    4. Theofilakou, Nancy & Stournaras, Yannis, 2012. "Current account adjustments in OECD countries revisited: The role of the fiscal stance," Journal of Policy Modeling, Elsevier, vol. 34(5), pages 719-734.
    5. Badarau-Semenescu, Cristina & Ndiaye, Cheikh Tidiane, 2010. "Politique économique et transmission des chocs dans la zone euro," L'Actualité Economique, Société Canadienne de Science Economique, vol. 86(1), pages 35-77, mars.
    6. Fresoli, Diego & Poncela, Pilar & Ruiz, Esther, 2023. "Ignoring cross-correlated idiosyncratic components when extracting factors in dynamic factor models," Economics Letters, Elsevier, vol. 230(C).
    7. McElroy, Tucker S. & Wildi, Marc, 2020. "The Multivariate Linear Prediction Problem: Model-Based and Direct Filtering Solutions," Econometrics and Statistics, Elsevier, vol. 14(C), pages 112-130.

  8. Mario Mazzocchi & Davide Delle Monache & Alexandra Lobb, 2006. "A structural time series approach to modelling multiple and resurgent meat scares in Italy," Applied Economics, Taylor & Francis Journals, vol. 38(14), pages 1677-1688.

    Cited by:

    1. Rieger, Jörg & Kuhlgatz, Christian & Anders, Sven, 2016. "Food scandals, media attention and habit persistence among desensitised meat consumers," Food Policy, Elsevier, vol. 64(C), pages 82-92.
    2. Alessandro Corsi & Silvia Novelli, 2011. "Willingness-to-pay in terms of price: an application to organic beef during and after the “mad cow” crisis," Review of Agricultural and Environmental Studies - Revue d'Etudes en Agriculture et Environnement, INRA Department of Economics, vol. 92(1), pages 25-46.
    3. Xavier IRZ & Mario MAZZOCCHI & Vincent RÉQUILLART & Louis-Georges SOLER, 2015. "Research in Food Economics: past trends and new challenges," Review of Agricultural and Environmental Studies - Revue d'Etudes en Agriculture et Environnement, INRA Department of Economics, vol. 96(1), pages 187-237.
    4. Beach, Robert H. & Zhen, Chen, 2009. "Consumer Purchasing Behavior in Response to Media Coverage of Avian Influenza," 2009 Conference, August 16-22, 2009, Beijing, China 51742, International Association of Agricultural Economists.
    5. Shashika D. Rathnayaka & Saroja Selvanathan & E. A. Selvanathan, 2021. "Demand for animal‐derived food in selected Asian countries: A system‐wide analysis," Agricultural Economics, International Association of Agricultural Economists, vol. 52(1), pages 97-122, January.
    6. Mu, Jianhong E. & McCarl, Bruce A. & Bessler, David A., 2013. "Impacts of BSE and Avian Influenza on U.S. Meat Demand," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150392, Agricultural and Applied Economics Association.

Chapters

  1. Davide Delle Monache & Ivan Petrella & Fabrizio Venditti, 2016. "Common Faith or Parting Ways? A Time Varying Parameters Factor Analysis of Euro-Area Inflation," Advances in Econometrics, in: Dynamic Factor Models, volume 35, pages 539-565, Emerald Group Publishing Limited.
    See citations under working paper version above.Sorry, no citations of chapters recorded.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 24 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-MAC: Macroeconomics (18) 2014-07-21 2014-07-28 2016-03-23 2016-05-08 2016-11-13 2016-12-11 2016-12-18 2017-03-05 2017-03-19 2017-07-23 2018-03-19 2019-11-11 2019-12-02 2020-08-10 2020-08-17 2020-11-23 2021-03-29 2021-05-31. Author is listed
  2. NEP-FOR: Forecasting (9) 2014-07-21 2014-07-28 2016-03-23 2016-12-11 2016-12-18 2020-08-10 2020-08-17 2021-03-29 2021-05-31. Author is listed
  3. NEP-ETS: Econometric Time Series (8) 2015-06-27 2015-07-04 2016-11-13 2016-12-11 2016-12-18 2017-03-19 2019-01-14 2020-02-24. Author is listed
  4. NEP-ORE: Operations Research (7) 2014-07-21 2014-07-28 2015-06-27 2015-07-04 2016-11-13 2019-11-11 2020-11-23. Author is listed
  5. NEP-ECM: Econometrics (5) 2014-07-21 2015-06-27 2016-11-13 2016-12-18 2019-01-14. Author is listed
  6. NEP-EEC: European Economics (5) 2016-05-08 2017-03-05 2017-07-23 2018-03-19 2023-08-21. Author is listed
  7. NEP-MON: Monetary Economics (5) 2016-05-08 2017-03-05 2018-03-19 2019-12-02 2023-08-21. Author is listed
  8. NEP-CBA: Central Banking (4) 2016-05-08 2017-03-05 2018-03-19 2019-12-02
  9. NEP-RMG: Risk Management (4) 2020-08-10 2020-08-17 2021-03-29 2021-05-31
  10. NEP-CFN: Corporate Finance (2) 2019-11-11 2020-11-23
  11. NEP-FDG: Financial Development and Growth (2) 2020-08-10 2021-03-29
  12. NEP-CWA: Central and Western Asia (1) 2021-05-31
  13. NEP-ENE: Energy Economics (1) 2023-08-21
  14. NEP-PKE: Post Keynesian Economics (1) 2016-05-08

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