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Business cycle dynamics after the Great Recession: An Extended Markov-Switching Dynamic Factor Model

Author

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  • Catherine Doz

    () (PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Panthéon-Sorbonne - ENS Paris - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, PSE - Paris School of Economics)

  • Laurent Ferrara

    () (SKEMA Business School, EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique)

  • Pierre-Alain Pionnier

    (OCDE - Organisation de Coopération et de Développement Economiques)

Abstract

The Great Recession and the subsequent period of subdued GDP growth in most advanced economies have highlighted the need for macroeconomic forecasters to account for sudden and deep recessions, periods of higher macroeconomic volatility, and fluctuations in trend GDP growth. In this paper, we put forward an extension of the standard Markov-Switching Dynamic Factor Model (MS-DFM) by incorporating two new features: switches in volatility and time-variation in trend GDP growth. First, we show that volatility switches largely improve the detection of business cycle turning points in the low-volatility environment prevailing since the mid-1980s. It is an important result for the detection of future recessions since, according to our model, the US economy is now back to a low-volatility environment after an interruption during the Great Recession. Second, our model also captures a continuous decline in the US trend GDP growth that started a few years before the Great Recession and continued thereafter. These two extensions of the standard MS-DFM framework are supported by information criteria, marginal likelihood comparisons and improved real-time GDP forecasting performance.

Suggested Citation

  • Catherine Doz & Laurent Ferrara & Pierre-Alain Pionnier, 2020. "Business cycle dynamics after the Great Recession: An Extended Markov-Switching Dynamic Factor Model," PSE Working Papers halshs-02443364, HAL.
  • Handle: RePEc:hal:psewpa:halshs-02443364
    Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-02443364
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    References listed on IDEAS

    as
    1. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
    2. Bauwens, Luc & Rombouts, Jeroen V.K., 2012. "On marginal likelihood computation in change-point models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3415-3429.
    3. Dale W. Jorgenson & Mun S. Ho & Kevin J. Stiroh, 2008. "A Retrospective Look at the U.S. Productivity Growth Resurgence," Journal of Economic Perspectives, American Economic Association, vol. 22(1), pages 3-24, Winter.
    4. Marianne Sensier & Dick van Dijk, 2004. "Testing for Volatility Changes in U.S. Macroeconomic Time Series," The Review of Economics and Statistics, MIT Press, vol. 86(3), pages 833-839, August.
    5. Joseph Stiglitz & Jean-Paul Fitoussi & Martine Durand, 2018. "Beyond GDP: Measuring What Counts for Economic and Social Performance," Sciences Po publications info:hdl:2441/4vsqk7docb9, Sciences Po.
    6. Yunjong Eo & Chang-Jin Kim, 2016. "Markov-Switching Models with Evolving Regime-Specific Parameters: Are Postwar Booms or Recessions All Alike?," The Review of Economics and Statistics, MIT Press, vol. 98(5), pages 940-949, December.
    7. Michael W. McCracken & Serena Ng, 2016. "FRED-MD: A Monthly Database for Macroeconomic Research," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 574-589, October.
    8. Amélie Charles & Olivier Darné & Laurent Ferrara, 2018. "Does The Great Recession Imply The End Of The Great Moderation? International Evidence," Economic Inquiry, Western Economic Association International, vol. 56(2), pages 745-760, April.
    9. James H. Stock & Mark W. Watson, 2003. "Has the Business Cycle Changed and Why?," NBER Chapters, in: NBER Macroeconomics Annual 2002, Volume 17, pages 159-230, National Bureau of Economic Research, Inc.
    10. Sylvia Kaufmann, 2000. "Measuring business cycles with a dynamic Markov switching factor model: an assessment using Bayesian simulation methods," Econometrics Journal, Royal Economic Society, vol. 3(1), pages 39-65.
    11. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    12. Diebold, Francis X & Rudebusch, Glenn D, 1996. "Measuring Business Cycles: A Modern Perspective," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 67-77, February.
    13. Giordani, Paolo & Kohn, Robert & van Dijk, Dick, 2007. "A unified approach to nonlinearity, structural change, and outliers," Journal of Econometrics, Elsevier, vol. 137(1), pages 112-133, March.
    14. Chan, Joshua C.C. & Grant, Angelia L., 2016. "Fast computation of the deviance information criterion for latent variable models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 847-859.
    15. S. J. Koopman & J. Durbin, 2003. "Filtering and smoothing of state vector for diffuse state‐space models," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(1), pages 85-98, January.
    16. Margaret M. McConnell & Gabriel Perez-Quiros, 2000. "Output fluctuations in the United States: what has changed since the early 1980s?," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
    17. Massimiliano Marcellino & Mario Porqueddu & Fabrizio Venditti, 2016. "Short-Term GDP Forecasting With a Mixed-Frequency Dynamic Factor Model With Stochastic Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 118-127, January.
    18. 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.
    19. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1, June.
    20. Zidong An & João Tovar Jalles & Prakash Loungani, 2018. "How well do economists forecast recessions?," International Finance, Wiley Blackwell, vol. 21(2), pages 100-121, June.
    21. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    22. 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.
    23. María Dolores Gadea & Ana Gómez‐Loscos & Gabriel Pérez‐Quirós, 2018. "Great Moderation And Great Recession: From Plain Sailing To Stormy Seas?," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(4), pages 2297-2321, November.
    24. Chauvet, Marcelle, 1998. "An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime Switching," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 969-996, November.
    25. Amélie Charles & Olivier Darné & Laurent Ferrara, 2018. "Does The Great Recession Imply The End Of The Great Moderation? International Evidence," Economic Inquiry, Western Economic Association International, vol. 56(2), pages 745-760, April.
    26. Camacho, Maximo & Perez-Quiros, Gabriel & Poncela, Pilar, 2018. "Markov-switching dynamic factor models in real time," International Journal of Forecasting, Elsevier, vol. 34(4), pages 598-611.
    27. Jushan Bai & Peng Wang, 2011. "Conditional Markov chain and its application in economic time series analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(5), pages 715-734, August.
    28. J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
    29. Zidong An & João Tovar Jalles & Prakash Loungani, 2018. "How well do economists forecast recessions?," International Finance, Wiley Blackwell, vol. 21(2), pages 100-121, June.
    30. Harding, Don & Pagan, Adrian, 2006. "Synchronization of cycles," Journal of Econometrics, Elsevier, vol. 132(1), pages 59-79, May.
    31. Juan Antolin-Diaz & Thomas Drechsel & Ivan Petrella, 2017. "Tracking the Slowdown in Long-Run GDP Growth," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 343-356, May.
    32. Camacho, Maximo & Perez Quiros, Gabriel & Poncela, Pilar, 2014. "Green shoots and double dips in the euro area: A real time measure," International Journal of Forecasting, Elsevier, vol. 30(3), pages 520-535.
    33. Marcelle Chauvet & James D. Hamilton, 2005. "Dating Business Cycle Turning Points," NBER Working Papers 11422, National Bureau of Economic Research, Inc.
    34. Chauvet, Marcelle & Senyuz, Zeynep & Yoldas, Emre, 2015. "What does financial volatility tell us about macroeconomic fluctuations?," Journal of Economic Dynamics and Control, Elsevier, vol. 52(C), pages 340-360.
    35. John G. Fernald & Robert E. Hall & James H. Stock & Mark W. Watson, 2017. "The Disappointing Recovery of Output after 2009," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 48(1 (Spring), pages 1-81.
    36. Chang-Jin Kim & Charles R. Nelson, 1998. "Business Cycle Turning Points, A New Coincident Index, And Tests Of Duration Dependence Based On A Dynamic Factor Model With Regime Switching," The Review of Economics and Statistics, MIT Press, vol. 80(2), pages 188-201, May.
    37. Frühwirth-Schnatter, Sylvia & Wagner, Helga, 2008. "Marginal likelihoods for non-Gaussian models using auxiliary mixture sampling," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4608-4624, June.
    38. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    39. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409, National Bureau of Economic Research, Inc.
    40. Hamilton, James D., 2011. "Calling recessions in real time," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1006-1026, October.
    41. 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.
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    Cited by:

    1. Bram van Os & Dick van Dijk, 2020. "Accelerating Peak Dating in a Dynamic Factor Markov-Switching Model," Tinbergen Institute Discussion Papers 20-057/VI, Tinbergen Institute.

    More about this item

    Keywords

    Markov-Switching Dynamic Factor Model (MS-DFM); Great Moderation; Great Recession; Turning-Point Detection; Macroeconomic Forecasting;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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