This paper evaluates the ability of a statistical regime-switching model to identify turning points in U.S. economic activity in real time. The authors work with Markov-switching models of real GDP and employment that, when estimated on the entire post-war sample, provide a chronology of business cycle peak and trough dates very close to that produced by the National Bureau of Economic Research (NBER). Next, they investigate how accurately and quickly the models would have identified turning points had they been used in real-time for the past forty years. In general, the models identify turning point dates in real-time that are close to the NBER dates. For both business cycle peaks and troughs, the models provide systematic improvement over the NBER in the speed at which turning points are identified. Importantly, the models achieve this with few instances of “false positives.” Overall, the evidence suggests that the regime-switching model could be a useful supplement to the NBER Business Cycle Dating Committee for establishing turning point dates. The model appears to capture the features of the NBER chronology in an accurate, timely way, and does so in a transparent and consistent fashion.
JEL classification: C32, C50, E3
Keywords: business cycles, real time forecasting, Markov switching, turning points
The authors have received helpful comments from James Bullard, James Hamilton, and James Morley. Mrinalini Lhila provided excellent research assistance. The views expressed here are the authors’ and not necessarily those of the Federal Reserve Bank of Atlanta or the Federal Reserve System. Any remaining errors are the authors’ responsibility.
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