A Comparison of the Real-Time Performance of Business Cycle Dating Methods

Abstract

This paper evaluates the ability of formal rules to establish U.S. business cycle turning point dates in real time. We consider two approaches, a nonparametric algorithm and a parametric Markov-switching dynamic-factor model. In order to accurately assess the real-time performance of these rules, we construct a new unrevised "real-time" data set of employment, industrial production, manufacturing and trade sales, and personal income. We then apply the rules to this data set to simulate the accuracy and timeliness with which they would have identified the NBER business cycle chronology had they been used in real time for the past 30 years. Both approaches accurately identified the NBER dated turning points in the sample in real time, with no instances of false positives. Further, both approaches, and especially the Markovswitching model, yielded significant improvement over the NBER in the speed with which business cycle troughs were identified. In addition to suggesting that business cycle dating rules are an informative tool to use alongside the traditional NBER analysis, these results provide formal evidence regarding the speed with which macroeconomic data reveals information about new business cycle phases.

Time‐Varying Fund Manager Skill

“…They have an advantage over the NBER recession indicator variable in that they are continuous measures of the strength of the economy, rather than a coarser discrete measure. The first, RecRT , is the real‐time recession probability measure constructed by Chauvet and Piger () . The second indicator, RecCFNAI , is the Chicago Fed National Activity Index (CFNAI) multiplied by −1; RecCFNAI is negative when economic activity is above average and is positive when economic activity is below average .…”

“…We normalize Timing and Picking so that each has a mean of zero and a standard deviation of one in the cross‐section, each period. Next, we set the weight on Timing equal to , where is the real‐time recession probability of Chauvet and Piger (). This continuous weighting scheme is quite intuitive: linearly weight Timing more whenever the probability of a recession increases.…”

“…Real‐time recession probabilities for the United States are obtained from a dynamic‐factor Markov‐switching model applied to four monthly coincident variables: nonfarm payroll employment, the index of industrial production, real personal income excluding transfer payments, and real manufacturing and trade sales. An analysis of the performance of this model for dating business cycles in real time and more details are in Chauvet and Piger (). Results are similar for the real‐time recession probability measure from the Survey of Professional Forecasters (see the Internet Appendix).…”

Forecasting National Recessions Using State‐Level Data

“…Since the inception of the NBER's Business Cycle Dating Committee in 1978, the NBER has announced new business cycle peaks with a lag of 7.5 months and troughs with a lag of 15 months. Chauvet and Piger () show that statistical models estimated using aggregate data improve on the NBER's timeliness for troughs, but not for peaks.…”

Predicting Recessions with Factor Linear Dynamic Harmonic Regressions

“…See Marcellino () for a comprehensive review of the literature. Some applications can be seen in Chauvet and Piger (), among others.…”