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Dating Business Cycle Turning Points for the French Economy: a MS-DFM approach

Listed author(s):
  • Catherine Doz

    ()

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics)

  • Anna Petronevich

    ()

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics)

The official institutions (NBER, OECD, CEPR and others) provide business cycle chronology with a lag from 3 months up to several years. Markov-Switching Dynamic Factor Model (MS-DFM) allows to produce the turning points more timely. The Kalman filter estimates of the model can be obtained in one step with limited number of series or in two steps on a much richer dataset. While the choice of correct series is a challenge for the one-step method, the problem of the two-step method is the potential misspecification. In this paper we apply one-step and two-step approaches to the French data and compare their performance. Both methods give qualitatively similar results and prove to reproduce the OECSD business cycle chronology on the 1993-2014 monthly sample well. We find that the two-step method is more precise in determining the beginnings and the ends of recessions. Also, both methods produce extra signals corresponding to downturns which were too short to belong to OECD chronology of recessions.

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Paper provided by HAL in its series Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) with number hal-01159200.

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Date of creation: Feb 2015
Publication status: Published in Documents de travail du Centre d'Economie de la Sorbonne 2015.09 - ISSN : 1955-611X. 2015
Handle: RePEc:hal:cesptp:hal-01159200
Note: View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-01159200
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  1. Michael J. Dueker & Martin Sola, 2008. "Multivariate Markov switching with weighted regime determination: giving France more weight than Finland," Working Papers 2008-001, Federal Reserve Bank of St. Louis.
  2. Stock, James H. & Watson, Mark W., 2014. "Estimating turning points using large data sets," Journal of Econometrics, Elsevier, vol. 178(P2), pages 368-381.
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  8. Doz, Catherine & Giannone, Domenico & Reichlin, Lucrezia, 2011. "A two-step estimator for large approximate dynamic factor models based on Kalman filtering," Journal of Econometrics, Elsevier, vol. 164(1), pages 188-205, September.
  9. Stéphane Grégoir & Fabrice Lenglart, 1998. "Measuring the Probability of a Business Cycle Turning Point by Using a Multivariate Qualitative Hidden Markov Model," Working Papers 98-48, Center for Research in Economics and Statistics.
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  12. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1014-1024, November.
  13. Olivier Darné & Laurent Ferrara, 2011. "Identification of Slowdowns and Accelerations for the Euro Area Economy," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(3), pages 335-364, June.
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  15. repec:ebl:ecbull:v:3:y:2002:i:25:p:1-17 is not listed on IDEAS
  16. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
  17. Chauvet, Marcelle & Senyuz, Zeynep, 2008. "A Joint Dynamic Bi-Factor Model of the Yield Curve and the Economy as a Predictor of Business Cycles," MPRA Paper 15076, University Library of Munich, Germany, revised Apr 2009.
  18. Xiaoshan Chen, 2007. "Evaluating the Synchronisation of the Eurozone Business Cycles using Multivariate Coincident Macroeconomic Indicators," Discussion Paper Series 2007_27, Department of Economics, Loughborough University, revised Nov 2007.
  19. Konstantin A., KHOLODILIN & Wension Vincent, YAO, 2004. "Business Cycle Turning Points : Mixed-Frequency Data with Structural Breaks," Discussion Papers (IRES - Institut de Recherches Economiques et Sociales) 2004024, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
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