Location of Repository

Estimating Turning Points Using Large Data Sets

By James H. Stock and Mark W. Watson


Dating business cycles entails ascertaining economy-wide turning points. Broadly speaking, there are two approaches in the literature. The first approach, which dates to Burns and Mitchell (1946), is to identify turning points individually in a large number of series, then to look for a common date that could be called an aggregate turning point. The second approach, which has been the focus of more recent academic and applied work, is to look for turning points in a few, or just one, aggregate. This paper examines these two approaches to the identification of turning points. We provide a nonparametric definition of a turning point (an estimand) based on a population of time series. This leads to estimators of turning points, sampling distributions, and standard errors for turning points based on a sample of series. We consider both simple random sampling and stratified sampling. The empirical part of the analysis is based on a data set of 270 disaggregated monthly real economic time series for the U.S., 1959-2010.

OAI identifier:

Suggested articles



  1. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle,”
  2. (2010). Calling Recessions in Real Time,”
  3. (2008). Cycle Dating Committee,
  4. (1946). Measuring Business Cycles.
  5. (1989). New Indexes of Coincident and Leading Economic Indicators,”
  6. (2003). On the Asymptotic Normality of Kernel Regression Estimators of the Mode in the Nonparametric Random Design Model,”
  7. (1988). On Weak Convergence and Optimal Kernel Density

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.