Document Type

Journal Article

Publication Date

Winter 12-2020


Agricultural Science


Hutson School of Agriculture


The Chow test is a 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. If the individual units within the groups have systematic differences, the Chow test is compromised: individual and group effects become confounded. This can cause rejections in the absence of the group effect of interest. We illustrate the problem with Monte Carlo analyses, and propose an alternative bootstrap-like testing procedure that helps eliminate excessive Type I errors.



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