Working Paper: NBER ID: w16532
Authors: James H. Stock; Mark W. Watson
Abstract: 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.
Keywords: business cycles; turning points; nonparametric methods; economic indicators
JEL Codes: C32; E32
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| nonparametric definition of turning points (C14) | more robust estimator (C51) |
| stratified sampling techniques (C83) | reduce bias introduced by sampling irregularities (C83) |
| estimation of turning points (C51) | improve upon previous studies (C90) |
| estimation of turning points (C51) | provide comprehensive analysis of timing of business cycles (F44) |
| asymptotic theory for kernel estimators (C51) | validate consistency of findings with existing business cycle chronologies (E32) |