The Chow Test with Time Series-Cross Section Data

25 Pages Posted: 1 Aug 2018 Last revised: 20 Jul 2021

See all articles by James K. Binkley

James K. Binkley

Purdue University - Department of Agricultural Economics

Jeffrey Young

Murray State University - Hutson School of Agriculture - Agribusiness

Date Written: May 31, 2018

Abstract

The Chow test is the standard method to test for differences in regression response across groups. In some cases, the groups being tested are composed of a time series of cross sections. For example, when testing for differences across industries, each industry may be composed of several observations on several individual firms. If the individuals themselves have systematic differences, the Chow test will be compromised: the individual and group effects become confounded. This can cause rejections in the absence of the group effect of interest. We illustrate the problem with a Monte Carlo analysis, and show that the effects cannot be separated. We propose a bootstrap-like testing procedure that can eliminate excessive Type I errors, and when used with the standard Chow test can help to arrive at an appropriate conclusion when both effects are present.

Suggested Citation

Binkley, James K. and Young, Jeffrey, The Chow Test with Time Series-Cross Section Data (May 31, 2018). Available at SSRN: https://ssrn.com/abstract=3212712 or http://dx.doi.org/10.2139/ssrn.3212712

James K. Binkley

Purdue University - Department of Agricultural Economics ( email )

West Lafayette, IN 47907-1145
United States
765-494 42613 (Phone)

Jeffrey Young (Contact Author)

Murray State University - Hutson School of Agriculture - Agribusiness ( email )

Murray, KY 42071-0009
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
309
Abstract Views
1,194
PlumX Metrics