Estimating turning points using large data sets

Mark W. Watson and James H. Stock

Scholarly Articles from Harvard University Department of Economics

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 US, 1959–2010.

Date: 2014
References: Add references at CitEc
Citations: View citations in EconPapers (81)

Published in Journal of Econometrics

Downloads: (external link)
http://dash.harvard.edu/bitstream/handle/1/3319219 ... Turning%20Points.pdf (application/pdf)
Our link check indicates that this URL is bad, the error code is: 404 Not found (http://dash.harvard.edu/bitstream/handle/1/33192198/Estimating%20Turning%20Points.pdf [301 Moved Permanently]--> https://dash.harvard.edu/bitstream/handle/1/33192198/Estimating%20Turning%20Points.pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hrv:faseco:33192198

Access Statistics for this paper

More papers in Scholarly Articles from Harvard University Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Office for Scholarly Communication ().