Several official institutions (NBER, OECD, CEPR, and others) provide business cycle chronologies with lags ranging from three months to several years. In this paper, we propose a Markov-switching dynamic factor model that allows for a more timely estimation of turning points. We apply one-step and two-step estimation approaches to French data and compare their performance. One-step maximum likelihood estimation is confined to relatively small data sets, whereas two-step approach that uses principal components can accommodate much bigger information sets. We find that both methods give qualitatively similar results and agree with the OECD dating of recessions on a sample of monthly data covering the period 1993–2014. The two-step method is more precise in determining the beginnings and ends of recessions as given by the OECD. Both methods indicate additional downturns in the French economy that were too short to enter the OECD chronology.
The authors thank the editors and two anonymous referees for useful remarks. All remaining errors are ours. We also acknowledge financial support by the European Commission in the framework of the European Doctorate in Economics – Erasmus Mundus (EDEEM).
Doz, C. and Petronevich, A. (2016), "Dating Business Cycle Turning Points for the French Economy: An MS-DFM approach", Dynamic Factor Models (Advances in Econometrics, Vol. 35), Emerald Group Publishing Limited, Leeds, pp. 481-538. https://doi.org/10.1108/S0731-905320150000035012
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