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Matteo Mogliani

Personal Details

First Name:Matteo
Middle Name:
Last Name:Mogliani
Suffix:
RePEc Short-ID:pmo475
[This author has chosen not to make the email address public]
http://sites.google.com/site/mmogliani/
Terminal Degree:2011 Paris School of Economics (from RePEc Genealogy)

Affiliation

Banque de France

Paris, France
http://www.banque-france.fr/
RePEc:edi:bdfgvfr (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Matteo Mogliani & Anna Simoni, 2024. "Bayesian Bi-level Sparse Group Regressions for Macroeconomic Density Forecasting," Papers 2404.02671, arXiv.org, revised Nov 2024.
  2. Laurent Ferrara & Matteo Mogliani & Jean-Guillaume Sahuc, 2020. "High-frequency monitoring of growth-at-risk," CAMA Working Papers 2020-97, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  3. Matteo Mogliani & Anna Simoni, 2019. "Bayesian MIDAS Penalized Regressions: Estimation, Selection, and Prediction," Papers 1903.08025, arXiv.org, revised Jun 2020.
  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. Mogliani, M. & Brunhes-Lesage, V. & Darné, O. & Pluyaud, B., 2014. "New estimate of the MIBA forecasting model. Modeling first-release GDP using the Banque de France's Monthly Business Survey and the “blocking” approach," Working papers 473, Banque de France.
  6. Bec, F. & Mogliani, M., 2013. "Nowcasting French GDP in Real-Time from Survey Opinions: Information or Forecast Combinations?," Working papers 436, Banque de France.
  7. Ferrara, L. & Marcellino, M. & Mogliani, M., 2012. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," Working papers 383, Banque de France.
  8. Druant, Martine & Vanhala, Juuso & Ktoris, Michalis & Jarvis, Valerie & Bouchet, Muriel & Budnik, Katarzyna & Childs, Claire & Kuttner, Nicole & Spooner, Magdalena & De Mulder, Jan & Bonthuis, Boele &, 2012. "Euro area labour markets and the crisis," Occasional Paper Series 138, European Central Bank.
  9. Matteo Mogliani, 2010. "Residual-based tests for cointegration and multiple deterministic structural breaks: A Monte Carlo study," PSE Working Papers halshs-00564897, HAL.
  10. Luiz de Mello & Matteo Mogliani, 2009. "Current Account Sustainability in Brazil: A Non-Linear Approach," OECD Economics Department Working Papers 703, OECD Publishing.
  11. Matteo Mogliani & Giovanni Urga & Carlos Winograd, 2009. "Monetary disorder and financial regimes - The demand for money in Argentina, 1900-2006," PSE Working Papers halshs-00575107, HAL.

Articles

  1. 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.
  2. Diev Pavel, & Kalantzis Yannick, & Lalliard Antoine, & Mogliani Matteo, 2021. "What explains the persistent weakness of euro area inflation since 2013? [Comment expliquer la faiblesse de l’inflation en zone euro depuis 2013 ?]," Bulletin de la Banque de France, Banque de France, issue 234.
  3. Mogliani, Matteo & Simoni, Anna, 2021. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Journal of Econometrics, Elsevier, vol. 222(1), pages 833-860.
  4. Clémence Berson & Louis de Charsonville & Pavel Diev & Violaine Faubert & Laurent Ferrara & Sophie Guilloux-Nefussi & Yannick Kalantzis & Antoine Lalliard & Julien Matheron & Matteo Mogliani, 2018. "Does the Phillips curve still exist?," Rue de la Banque, Banque de France, issue 56, february.
  5. Matteo Mogliani & Giovanni Urga, 2018. "On the Instability of Long‐Run Money Demand and the Welfare Cost of Inflation in the United States," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(7), pages 1645-1660, October.
  6. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
  7. 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.
  8. Ferrara, Laurent & Marcellino, Massimiliano & Mogliani, Matteo, 2015. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," International Journal of Forecasting, Elsevier, vol. 31(3), pages 664-679.
  9. de Mello Luiz & Moccero Diego & Mogliani Matteo, 2013. "Do Latin American Central Bankers Behave Non-Linearly? The Experiences of Brazil, Chile, Colombia and Mexico," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(2), pages 141-165, April.
  10. S. Haincourt. & M. Mogliani., 2012. "Has the 2008-2009 recession increased the structural share of unemployment in the euro area?," Quarterly selection of articles - Bulletin de la Banque de France, Banque de France, issue 25, pages 63-80, Spring.
  11. Haincourt, S. & Mogliani, M., 2012. "La récession de 2008-2009 a-t-elle accru la part structurelle du chômage en zone euro ?," Bulletin de la Banque de France, Banque de France, issue 187, pages 45-56.

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. Laurent Ferrara & Matteo Mogliani & Jean-Guillaume Sahuc, 2020. "High-frequency monitoring of growth-at-risk," CAMA Working Papers 2020-97, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.

    Cited by:

    1. Aaron J. Amburgey & Michael W. McCracken, 2023. "On the real‐time predictive content of financial condition indices for growth," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 137-163, March.
    2. 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.
    3. Chuliá, Helena & Garrón, Ignacio & Uribe, Jorge M., 2024. "Daily growth at risk: Financial or real drivers? The answer is not always the same," International Journal of Forecasting, Elsevier, vol. 40(2), pages 762-776.
    4. 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.
    5. Schick, Manuel, 2024. "Real-time Nowcasting Growth-at-Risk using the Survey of Professional Forecasters," Working Papers 0750, University of Heidelberg, Department of Economics.
    6. Lhuissier Stéphane, 2022. "Financial Conditions and Macroeconomic Downside Risks in the Euro Area," Working papers 863, Banque de France.
    7. Tibor Szendrei & Arnab Bhattacharjee, 2024. "Momentum Informed Inflation-at-Risk," Papers 2408.12286, arXiv.org.
    8. Katalin Varga & Tibor Szendrei, 2024. "Non-stationary Financial Risk Factors and Macroeconomic Vulnerability for the UK," Papers 2404.01451, arXiv.org.
    9. Geghetsik Afunts & Misina Cato & Tobias Schmidt, 2023. "Inflation Expectations in the Wake of the War in Ukraine," CERGE-EI Working Papers wp745, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    10. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2021. "Tail Forecasting with Multivariate Bayesian Additive Regression Trees," Working Papers 21-08R, Federal Reserve Bank of Cleveland, revised 12 Jul 2022.
    11. Eraslan, Sercan & Reif, Magnus, 2023. "A latent weekly GDP indicator for Germany," Technical Papers 08/2023, Deutsche Bundesbank.
    12. Zheng, Tingguo & Gong, Lu & Ye, Shiqi, 2023. "Global energy market connectedness and inflation at risk," Energy Economics, Elsevier, vol. 126(C).
    13. Philippe Goulet Coulombe & Karin Klieber & Christophe Barrette & Maximilian Goebel, 2024. "Maximally Forward-Looking Core Inflation," Papers 2404.05209, arXiv.org.
    14. Marian Vavra, 2023. "Bias-Correction in Time Series Quantile Regression Models," Working and Discussion Papers WP 3/2023, Research Department, National Bank of Slovakia.
    15. 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).
    16. Škrinjarić, Tihana, 2024. "Growth-at-risk for macroprudential policy stance assessment: a survey," Bank of England working papers 1075, Bank of England.
    17. Matteo Iacopini & Francesco Ravazzolo & Luca Rossini, 2022. "Bayesian Multivariate Quantile Regression with alternative Time-varying Volatility Specifications," Papers 2211.16121, arXiv.org, revised Aug 2024.
    18. 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.
    19. Tibor Szendrei & Arnab Bhattacharjee & Mark E. Schaffer, 2024. "MIDAS-QR with 2-Dimensional Structure," Papers 2406.15157, arXiv.org.
    20. Lang, Jan Hannes & Rusnák, Marek & Greiwe, Moritz, 2023. "Medium-term growth-at-risk in the euro area," Working Paper Series 2808, European Central Bank.
    21. Szendrei, Tibor & Varga, Katalin, 2023. "Revisiting vulnerable growth in the Euro Area: Identifying the role of financial conditions in the distribution," Economics Letters, Elsevier, vol. 223(C).
    22. 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).
    23. 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).
    24. Sokol, Andrej, 2021. "Fan charts 2.0: flexible forecast distributions with expert judgement," Working Paper Series 2624, European Central Bank.
    25. Barbaglia, Luca & Frattarolo, Lorenzo & Onorante, Luca & Pericoli, Filippo Maria & Ratto, Marco & Tiozzo Pezzoli, Luca, 2022. "Testing big data in a big crisis: Nowcasting under COVID-19," JRC Working Papers in Economics and Finance 2022-06, Joint Research Centre, European Commission.
    26. Sara Boni & Massimiliano Caporin & Francesco Ravazzolo, 2024. "Nowcasting Inflation at Quantiles: Causality from Commodities," BEMPS - Bozen Economics & Management Paper Series BEMPS102, Faculty of Economics and Management at the Free University of Bozen.
    27. Kohns, David & Potjagailo, Galina, 2023. "Flexible Bayesian MIDAS: time‑variation, group‑shrinkage and sparsity," Bank of England working papers 1025, Bank of England.

  2. Matteo Mogliani & Anna Simoni, 2019. "Bayesian MIDAS Penalized Regressions: Estimation, Selection, and Prediction," Papers 1903.08025, arXiv.org, revised Jun 2020.

    Cited by:

    1. Chuliá, Helena & Garrón, Ignacio & Uribe, Jorge M., 2024. "Daily growth at risk: Financial or real drivers? The answer is not always the same," International Journal of Forecasting, Elsevier, vol. 40(2), pages 762-776.
    2. Laurent Ferrara & Matteo Mogliani & Jean-Guillaume Sahuc, 2020. "High-frequency monitoring of growth-at-risk," CAMA Working Papers 2020-97, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    3. Ana Beatriz Galvão & Michael Owyang, 2022. "Forecasting low‐frequency macroeconomic events with high‐frequency data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1314-1333, November.
    4. Alexandra Bozhechkova & Urmat Dzhunkeev, 2024. "CLARA and CARLSON: Combination of Ensemble and Neural Network Machine Learning Methods for GDP Forecasting," Russian Journal of Money and Finance, Bank of Russia, vol. 83(3), pages 45-69, September.
    5. Babii, Andrii & Ghysels, Eric & Striaukas, Jonas, 2021. "Machine Learning Time Series Regressions With an Application to Nowcasting," LIDAM Reprints LFIN 2021010, Université catholique de Louvain, Louvain Finance (LFIN).
    6. Richard Schnorrenberger & Aishameriane Schmidt & Guilherme Valle Moura, 2024. "Harnessing Machine Learning for Real-Time Inflation Nowcasting," Working Papers 806, DNB.
    7. Jardet Caroline & Meunier Baptiste, 2020. "Nowcasting World GDP Growth with High-Frequency Data," Working papers 788, Banque de France.
    8. Matteo Iacopini & Aubrey Poon & Luca Rossini & Dan Zhu, 2022. "Bayesian Mixed-Frequency Quantile Vector Autoregression: Eliciting tail risks of Monthly US GDP," Papers 2209.01910, arXiv.org.
    9. Ziwei Mei & Zhentao Shi, 2022. "On LASSO for High Dimensional Predictive Regression," Papers 2212.07052, arXiv.org, revised Jan 2024.
    10. Jad Beyhum & Jonas Striaukas, 2023. "Factor-augmented sparse MIDAS regressions with an application to nowcasting," Papers 2306.13362, arXiv.org, revised Nov 2024.
    11. Tibor Szendrei & Arnab Bhattacharjee & Mark E. Schaffer, 2024. "MIDAS-QR with 2-Dimensional Structure," Papers 2406.15157, arXiv.org.
    12. Matteo Mogliani & Anna Simoni, 2024. "Bayesian Bi-level Sparse Group Regressions for Macroeconomic Density Forecasting," Papers 2404.02671, arXiv.org, revised Nov 2024.
    13. Alain Hecq & Marie Ternes & Ines Wilms, 2023. "Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions," Papers 2301.10592, arXiv.org, revised Nov 2024.
    14. Zheng, Tingguo & Fan, Xinyue & Jin, Wei & Fang, Kuangnan, 2024. "Words or numbers? Macroeconomic nowcasting with textual and macroeconomic data," International Journal of Forecasting, Elsevier, vol. 40(2), pages 746-761.
    15. Kohns, David & Potjagailo, Galina, 2023. "Flexible Bayesian MIDAS: time‑variation, group‑shrinkage and sparsity," Bank of England working papers 1025, Bank of England.
    16. Helena Chuliá & Ignacio Garrón & Jorge M. Uribe, 2022. ""Monitoring daily unemployment at risk"," IREA Working Papers 202211, University of Barcelona, Research Institute of Applied Economics, revised Jul 2022.

  3. 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.

    Cited by:

    1. 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.
    2. Clément Bortoli & Stéphanie Combes & Thomas Renault, 2018. "Nowcasting GDP Growth by Reading Newspapers," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03205161, HAL.
    3. Bruno Ducoudre & Paul Hubert & Guilhem Tabarly, 2020. "The state-dependence of output revisions," Working Papers hal-03403073, HAL.

  4. Mogliani, M. & Brunhes-Lesage, V. & Darné, O. & Pluyaud, B., 2014. "New estimate of the MIBA forecasting model. Modeling first-release GDP using the Banque de France's Monthly Business Survey and the “blocking” approach," Working papers 473, Banque de France.

    Cited by:

    1. C. Thubin & T. Ferrière & E. Monnet & M. Marx & V. Oung, 2016. "The PRISME model: can disaggregation on the production side help to forecast GDP?," Working papers 596, Banque de France.
    2. Cyrille Lenoel & Garry Young, 2020. "Real-time turning point indicators: Review of current international practices," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2020-05, Economic Statistics Centre of Excellence (ESCoE).
    3. E. Monnet & C. Thubin, 2017. "Construction crises and business cycle: consequences for GDP forecasts," Rue de la Banque, Banque de France, issue 39, february..
    4. Clément Bortoli & Stéphanie Combes & Thomas Renault, 2018. "Nowcasting GDP Growth by Reading Newspapers," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03205161, HAL.
    5. Bańbura, Marta & Belousova, Irina & Bodnár, Katalin & Tóth, Máté Barnabás, 2023. "Nowcasting employment in the euro area," Working Paper Series 2815, European Central Bank.
    6. Tomas Adam & Filip Novotny, 2018. "Assessing the External Demand of the Czech Economy: Nowcasting Foreign GDP Using Bridge Equations," Working Papers 2018/18, Czech National Bank.
    7. Daniel Roash & Tanya Suhoy, 2019. "Sentiment Indicators Based on a Short Business Tendency Survey," Bank of Israel Working Papers 2019.11, Bank of Israel.
    8. 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.
    9. Frédérique Bec & Matteo Mogliani, 2013. "Nowcasting French GDP in Real-Time from Survey Opinions : Information or Forecast Combinations ?," Working Papers 2013-21, Center for Research in Economics and Statistics.
    10. Cobb, Marcus P A, 2018. "Improving Underlying Scenarios for Aggregate Forecasts: A Multi-level Combination Approach," MPRA Paper 88593, University Library of Munich, Germany.
    11. Gerardin Mathilde, & Ranvier Martial., 2021. "Enrichment of the Banque de France’s monthly business survey: lessons from textual analysis of business leaders’ comments," Working papers 821, Banque de France.

  5. Bec, F. & Mogliani, M., 2013. "Nowcasting French GDP in Real-Time from Survey Opinions: Information or Forecast Combinations?," Working papers 436, Banque de France.

    Cited by:

    1. 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.
    2. Christian Gayer & Alessandro Girardi & Andreas Reuter, 2016. "Replacing Judgment by Statistics: Constructing Consumer Confidence Indicators on the basis of Data-driven Techniques. The Case of the Euro Area," Working Papers LuissLab 16125, Dipartimento di Economia e Finanza, LUISS Guido Carli.
    3. Mogliani, Matteo & Simoni, Anna, 2021. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Journal of Econometrics, Elsevier, vol. 222(1), pages 833-860.
    4. E. Monnet & C. Thubin, 2017. "Construction crises and business cycle: consequences for GDP forecasts," Rue de la Banque, Banque de France, issue 39, february..
    5. Shrub, Yuliya & Rieger, Jonas & Müller, Henrik & Jentsch, Carsten, 2022. "Text data rule - don't they? A study on the (additional) information of Handelsblatt data for nowcasting German GDP in comparison to established economic indicators," Ruhr Economic Papers 964, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    6. 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.
    7. 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.
    8. Carlos León & Fabio Ortega, 2018. "Nowcasting economic activity with electronic payments data: A predictive modeling approach," Borradores de Economia 1037, Banco de la Republica de Colombia.
    9. Boriss Siliverstovs, 2017. "Short-term forecasting with mixed-frequency data: a MIDASSO approach," Applied Economics, Taylor & Francis Journals, vol. 49(13), pages 1326-1343, March.

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

    Cited by:

    1. 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.
    2. Ana B. Galvão & Michael T. Owyang, 2014. "Financial stress regimes and the macroeconomy," Working Papers 2014-20, Federal Reserve Bank of St. Louis.
    3. Marcellino, Massimiliano & Kapetanios, George & Dendramis, Yiannis, 2020. "A Similarity-based Approach for Macroeconomic Forecasting," CEPR Discussion Papers 14469, C.E.P.R. Discussion Papers.
    4. Kurmaş Akdoğan, 2017. "Unemployment hysteresis and structural change in Europe," Empirical Economics, Springer, vol. 53(4), pages 1415-1440, December.
    5. Boris Blagov & Michael Funke & Richhild Moessner, 2015. "Modelling the time-variation in euro area lending spreads," BIS Working Papers 526, Bank for International Settlements.
    6. Zeyyad Mandalinci, 2015. "Forecasting Inflation in Emerging Markets: An Evaluation of Alternative Models," CReMFi Discussion Papers 3, CReMFi, School of Economics and Finance, QMUL.
    7. Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2012. "Forecasting UK GDP growth, inflation and interest rates under structural change: a comparison of models with time-varying parameters," Bank of England working papers 450, Bank of England.
    8. Kevin Moran & Simplice Aimé Nono & Imad Rherrad, 2018. "Forecasting with Many Predictors: How Useful are National and International Confidence Data?," Cahiers de recherche 1814, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    9. David Alaminos & M. Belén Salas & Manuel A. Fernández-Gámez, 2022. "Quantum Computing and Deep Learning Methods for GDP Growth Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 803-829, February.
    10. 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.
    11. Heinrich, Markus, 2020. "Does the Current State of the Business Cycle matter for Real-Time Forecasting? A Mixed-Frequency Threshold VAR approach," EconStor Preprints 219312, ZBW - Leibniz Information Centre for Economics.
    12. Claudia Foroni & Massimiliano Marcellino & Dalibor Stevanovic, 2020. "Forecasting the Covid-19 Recession and Recovery: Lessons from the Financial Crisis," CIRANO Working Papers 2020s-32, CIRANO.
    13. Liu, Ying & Wen, Long & Liu, Han & Song, Haiyan, 2024. "Predicting tourism recovery from COVID-19: A time-varying perspective," Economic Modelling, Elsevier, vol. 135(C).
    14. Amélie Charles & Olivier Darné & Laurent Ferrara, 2018. "Does the Great Recession imply the end of the Great Moderation? International evidence," Post-Print hal-01757081, HAL.
    15. Jörg Döpke & Ulrich Fritsche & Christian Pierdzioch, 2015. "Predicting Recessions With Boosted Regression Trees," Working Papers 2015-004, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    16. 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.).
    17. Kajal Lahiri & Liu Yang, 2023. "Predicting binary outcomes based on the pair-copula construction," Empirical Economics, Springer, vol. 64(6), pages 3089-3119, June.
    18. 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.
    19. Knut Lehre Seip & Yunus Yilmaz & Michael Schröder, 2019. "Comparing Sentiment- and Behavioral-Based Leading Indexes for Industrial Production: When Does Each Fail?," Economies, MDPI, vol. 7(4), pages 1-18, October.
    20. Caterina Forti Grazzini & Massimo Guidolin, 2013. "Forecasting yield spreads under crisis-induced multiple breakpoints," Applied Economics Letters, Taylor & Francis Journals, vol. 20(18), pages 1656-1664, December.
    21. Lenza, Michele & Moutachaker, Inès & Paredes, Joan, 2023. "Density forecasts of inflation: a quantile regression forest approach," Working Paper Series 2830, European Central Bank.
    22. Bartkus Algirdas, 2016. "A New Model with Regime Switching Errors: Forecasting Gdp in Times of Great Recession," Ekonomika (Economics), Sciendo, vol. 95(2), pages 7-29, February.
    23. Paul Ho, 2021. "Forecasting in the Absence of Precedent," Working Paper 21-10, Federal Reserve Bank of Richmond.
    24. Federico Lampis, 2016. "Forecasting the sectoral GVA of a small Spanish region," Economics and Business Letters, Oviedo University Press, vol. 5(2), pages 38-44.
    25. Schlösser, Alexander, 2020. "Forecasting industrial production in Germany: The predictive power of leading indicators," Ruhr Economic Papers 838, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    26. Mahmut Gunay, 2016. "Forecasting Turkish GDP Growth with Financial Variables and Confidence Indicators," CBT Research Notes in Economics 1614, Research and Monetary Policy Department, Central Bank of the Republic of Turkey.
    27. Jackson, Karen & Magkonis, Georgios, 2024. "Exchange rate predictability: Fact or fiction?," Journal of International Money and Finance, Elsevier, vol. 142(C).
    28. Di Caro, Paolo, 2014. "Regional recessions and recoveries in theory and practice: a resilience-based overview," MPRA Paper 60300, University Library of Munich, Germany.
    29. Rafael Ravnik, 2014. "Short-Term Forecasting of GDP under Structural Changes," Working Papers 40, The Croatian National Bank, Croatia.
    30. Kurmaş Akdoğan, 2015. "Asymmetric Behaviour of Inflation around the Target in Inflation-Targeting Countries," Scottish Journal of Political Economy, Scottish Economic Society, vol. 62(5), pages 486-504, November.
    31. Benjamin Garcia & Arsenios Skaperdas, 2017. "Inferring the Shadow Rate from Real Activity," Finance and Economics Discussion Series 2017-106, Board of Governors of the Federal Reserve System (U.S.).

  7. Druant, Martine & Vanhala, Juuso & Ktoris, Michalis & Jarvis, Valerie & Bouchet, Muriel & Budnik, Katarzyna & Childs, Claire & Kuttner, Nicole & Spooner, Magdalena & De Mulder, Jan & Bonthuis, Boele &, 2012. "Euro area labour markets and the crisis," Occasional Paper Series 138, European Central Bank.

    Cited by:

    1. Cláudia Duarte & José R. Maria & Sharmin Sazedj, 2019. "Trends and cycles under changing economic conditions," Working Papers w201918, Banco de Portugal, Economics and Research Department.
    2. Palmeira, Rafael & Pindado, Julio & Requejo, Ignacio, 2023. "How does employment protection legislation affect labor investment inefficiencies?," Research in International Business and Finance, Elsevier, vol. 66(C).
    3. Dany-Knedlik, Geraldine & Holtemöller, Oliver, 2018. "Inflation dynamics during the Financial Crisis in Europe: cross-sectional identification of long-run inflation expectations," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181520, Verein für Socialpolitik / German Economic Association.
    4. Xavier Jara Tamayo, Holguer & Simon, Agathe, 2021. "The income protection role of an EMU-wide unemployment insurance system: the case of atypical workers," EUROMOD Working Papers EM6/21, EUROMOD at the Institute for Social and Economic Research.
    5. Verdugo, Gregory, 2015. "Real Wage Cyclicality in the Eurozone Before and During the Great Recession: Evidence from Micro Data," IZA Discussion Papers 9469, Institute of Labor Economics (IZA).
    6. Arestis, Philip & Ferreiro, Jesus & Gómez, Carmen, 2020. "Quality of employment and employment protection. Effects of employment protection on temporary and permanent employment," Structural Change and Economic Dynamics, Elsevier, vol. 53(C), pages 180-188.
    7. Masuch, Klaus & Anderton, Robert & Setzer, Ralph & Benalal, Nicholai, 2018. "Structural policies in the euro area," Occasional Paper Series 210, European Central Bank.

  8. Matteo Mogliani, 2010. "Residual-based tests for cointegration and multiple deterministic structural breaks: A Monte Carlo study," PSE Working Papers halshs-00564897, HAL.

    Cited by:

    1. Skrobotov, Anton, 2021. "Structural breaks in cointegration models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 63, pages 117-141.

  9. Matteo Mogliani & Giovanni Urga & Carlos Winograd, 2009. "Monetary disorder and financial regimes - The demand for money in Argentina, 1900-2006," PSE Working Papers halshs-00575107, HAL.

    Cited by:

    1. Matteo Mogliani, 2010. "Residual-based tests for cointegration and multiple deterministic structural breaks: A Monte Carlo study," PSE Working Papers halshs-00564897, HAL.
    2. Rodolfo E. Manuelli & Juan I. Vizcaino, 2017. "Monetary Policy with Declining Deficits: Theory and an Application to Recent Argentine Monetary Policy," Review, Federal Reserve Bank of St. Louis, vol. 99(4), pages 351-375.
    3. Georgina M. Gómez, 2019. "Money as an Institution: Rule versus Evolved Practice? Analysis of Multiple Currencies in Argentina," JRFM, MDPI, vol. 12(2), pages 1-14, May.

Articles

  1. 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.
    See citations under working paper version above.
  2. Diev Pavel, & Kalantzis Yannick, & Lalliard Antoine, & Mogliani Matteo, 2021. "What explains the persistent weakness of euro area inflation since 2013? [Comment expliquer la faiblesse de l’inflation en zone euro depuis 2013 ?]," Bulletin de la Banque de France, Banque de France, issue 234.

    Cited by:

    1. Olivier De Bandt & Juan Carluccio, 2022. "How globalisation affects inflation [La mondialisation et ses répercussions sur l’inflation]," Bulletin de la Banque de France, Banque de France, issue 240.

  3. Mogliani, Matteo & Simoni, Anna, 2021. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Journal of Econometrics, Elsevier, vol. 222(1), pages 833-860.
    See citations under working paper version above.
  4. Clémence Berson & Louis de Charsonville & Pavel Diev & Violaine Faubert & Laurent Ferrara & Sophie Guilloux-Nefussi & Yannick Kalantzis & Antoine Lalliard & Julien Matheron & Matteo Mogliani, 2018. "Does the Phillips curve still exist?," Rue de la Banque, Banque de France, issue 56, february.

    Cited by:

    1. Siena Daniele, & Zago Riccardo., 2021. "Job Polarization and the Flattening of the Price Phillips Curve," Working papers 819, Banque de France.
    2. Camatte Hadrien & Faubert Violaine & Lalliard Antoine & Daudin Guillaume & Rifflart Christine, 2021. "Global Value Chains and the transmission of exchange rate shocks to consumer prices," Working papers 797, Banque de France.
    3. Thibault Lemaire, 2020. "Phillips in A Revolution: Unemployment and Prices in Early 21st Century Egypt," Working Papers 1453, Economic Research Forum, revised 20 Dec 2020.
    4. Koester, Gerrit & Lis, Eliza & Nickel, Christiane & Osbat, Chiara & Smets, Frank, 2021. "Understanding low inflation in the euro area from 2013 to 2019: cyclical and structural drivers," Occasional Paper Series 280, European Central Bank.

  5. Matteo Mogliani & Giovanni Urga, 2018. "On the Instability of Long‐Run Money Demand and the Welfare Cost of Inflation in the United States," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(7), pages 1645-1660, October.

    Cited by:

    1. Tsutomu Watanabe & Tomoyoshi Yabu, 2018. "The Demand for Money at the Zero Interest Rate Bound," Working Papers on Central Bank Communication 002, University of Tokyo, Graduate School of Economics.
    2. Karsten Schweikert, 2022. "Oracle Efficient Estimation of Structural Breaks in Cointegrating Regressions," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(1), pages 83-104, January.
    3. Tsutomu Watanabe & Tomoyoshi Yabu, 2021. "Japan’s voluntary lockdown: further evidence based on age-specific mobile location data," The Japanese Economic Review, Springer, vol. 72(3), pages 333-370, July.
    4. Serletis, Apostolos & Xu, Libo, 2021. "The welfare cost of inflation," Journal of Economic Dynamics and Control, Elsevier, vol. 128(C).
    5. Serletis, Apostolos & Xu, Libo, 2023. "Consumer preferences, the demand for Divisia money, and the welfare costs of inflation," Journal of Macroeconomics, Elsevier, vol. 75(C).
    6. Dai, Wei & Serletis, Apostolos, 2019. "On the Markov switching welfare cost of inflation," Journal of Economic Dynamics and Control, Elsevier, vol. 108(C).
    7. Aleksander Berentsen & Samuel Huber & Alessandro Marchesiani, 2015. "Limited commitment and the demand for money," ECON - Working Papers 199, Department of Economics - University of Zurich, revised Feb 2016.
    8. Mohitosh Kejriwal & Pierre Perron & Xuewen Yu, 2020. "A Two Step Procedure for Testing Partial Parameter Stability in Cointegrated Regression Models," Boston University - Department of Economics - Working Papers Series WP2020-011, Boston University - Department of Economics.
    9. Curran, Michael & Dressler, Scott J., 2020. "Preferences, inflation, and welfare," European Economic Review, Elsevier, vol. 130(C).
    10. Amir Kia, 2024. "Demand for Money in the United States: Stability and Forward-Looking Tests," Economies, MDPI, vol. 12(2), pages 1-18, February.
    11. Karsten Schweikert, 2020. "Oracle Efficient Estimation of Structural Breaks in Cointegrating Regressions," Papers 2001.07949, arXiv.org, revised Apr 2021.
    12. Ufuk CAN & Zeynep Gizem CAN & Süleyman DEĞİRMEN, 2019. "Paranın Dolaşım Hızının ve Para Talebi Fonksiyonunun Ekonometrik Analizi: Türkiye Örneği," Istanbul Business Research, Istanbul University Business School, vol. 48(2), pages 218-247, November.

  6. Mogliani, Matteo & Darné, Olivier & Pluyaud, Bertrand, 2017. "The new MIBA model: Real-time nowcasting of French GDP using the Banque de France's monthly business survey," Economic Modelling, Elsevier, vol. 64(C), pages 26-39.
    See citations under working paper version above.
  7. 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.
    See citations under working paper version above.
  8. Ferrara, Laurent & Marcellino, Massimiliano & Mogliani, Matteo, 2015. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," International Journal of Forecasting, Elsevier, vol. 31(3), pages 664-679.
    See citations under working paper version above.
  9. de Mello Luiz & Moccero Diego & Mogliani Matteo, 2013. "Do Latin American Central Bankers Behave Non-Linearly? The Experiences of Brazil, Chile, Colombia and Mexico," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 17(2), pages 141-165, April.

    Cited by:

    1. baaziz, yosra, 2016. "Les règles de Taylor à l’épreuve de la révolution : cas de l’Égypte [The Taylor rule to the test of the revolution: the case of Egypt]," MPRA Paper 69779, University Library of Munich, Germany.
    2. Stefano Puddu, 2013. "Real Sector and Banking System: Real and Feedback Effects. A Non-Linear VAR Approach," IRENE Working Papers 13-01, IRENE Institute of Economic Research.
    3. Gabriel Caldas Montes & Caio Ferrari Ferreira, 2019. "Does monetary policy credibility mitigate the effects of uncertainty about exchange rate on uncertainties about both inflation and interest rate?," International Economics and Economic Policy, Springer, vol. 16(4), pages 649-678, October.
    4. Sánchez-Fung, José R., 2011. "Estimating monetary policy reaction functions for emerging market economies: The case of Brazil," Economic Modelling, Elsevier, vol. 28(4), pages 1730-1738, July.
    5. Lebogang Mateane & Christian R. Proaño, 2020. "Does monetary policy react asymmetrically to exchange rate misalignments? Evidence for South Africa," Empirical Economics, Springer, vol. 58(4), pages 1639-1658, April.
    6. Caldas Montes, Gabriel & Ferrari Ferreira, Caio, 2019. "Effect of monetary policy credibility on the fear of floating: Evidence from Brazil," Journal of Policy Modeling, Elsevier, vol. 41(5), pages 981-1004.

  10. S. Haincourt. & M. Mogliani., 2012. "Has the 2008-2009 recession increased the structural share of unemployment in the euro area?," Quarterly selection of articles - Bulletin de la Banque de France, Banque de France, issue 25, pages 63-80, Spring.

    Cited by:

    1. Dag Kolsrud, 2018. "Mismatch in the Norwegian Labour Market 2003–2013: Did Immigrants Make a Difference?," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 137(3), pages 979-997, June.

  11. Haincourt, S. & Mogliani, M., 2012. "La récession de 2008-2009 a-t-elle accru la part structurelle du chômage en zone euro ?," Bulletin de la Banque de France, Banque de France, issue 187, pages 45-56.

    Cited by:

    1. A. Maravalle. & M.-E. de la Serve. & G. Verdugo., 2014. "The euro area Beveridge curve in the post-crisis period: increase in structural unemployment since 2010," Quarterly selection of articles - Bulletin de la Banque de France, Banque de France, issue 36, pages 95-109, winter.

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 10 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-FOR: Forecasting (6) 2012-06-13 2013-04-13 2013-06-30 2019-03-25 2019-04-01 2024-05-13. Author is listed
  2. NEP-MAC: Macroeconomics (5) 2011-03-19 2012-06-13 2016-09-18 2019-04-01 2020-12-07. Author is listed
  3. NEP-BIG: Big Data (3) 2019-03-25 2019-04-01 2024-05-13
  4. NEP-ETS: Econometric Time Series (3) 2011-02-19 2019-03-25 2024-05-13
  5. NEP-ECM: Econometrics (2) 2019-03-25 2024-05-13
  6. NEP-EEC: European Economics (2) 2016-09-18 2020-12-07
  7. NEP-ORE: Operations Research (2) 2013-04-13 2019-04-01
  8. NEP-CBA: Central Banking (1) 2011-03-19
  9. NEP-CIS: Confederation of Independent States (1) 2011-02-19
  10. NEP-CMP: Computational Economics (1) 2019-04-01
  11. NEP-FDG: Financial Development and Growth (1) 2019-04-01
  12. NEP-HIS: Business, Economic and Financial History (1) 2011-03-19
  13. NEP-MIC: Microeconomics (1) 2011-02-19
  14. NEP-MON: Monetary Economics (1) 2011-03-19
  15. NEP-RMG: Risk Management (1) 2020-12-07
  16. NEP-SOG: Sociology of Economics (1) 2016-09-18

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