Identifying business cycle turning points in real time

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 a Markov-switching model fit to real gross domestic product and employment data that, when estimated on the entire postwar sample, provides a chronology of business cycle peak and trough dates close to that produced by the National Bureau of Economic Research (NBER). Next, they investigate how accurately and quickly the model would have identified NBER-dated turning points had it been used in real time for the past 40 years. In general, the model identifies turning point dates in real time that are close to the NBER dates. For both business cycle peaks and troughs, the model provides systematic improvement over the NBER in the speed at which turning points are identified. Importantly, the model achieves 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. It appears to capture the features of the NBER chronology accurately and swiftly; furthermore, the method is transparent and consistent.