Stacked linear regression analysis to facilitate testing of hypotheses across OLS regressions

Abstract

In empirical work, researchers frequently test hypotheses of parallel form in several regressions, which raises concerns about multiple testing. One way to address the multiple-testing issue is to jointly test the hypotheses (for example, Pei, Pischke, and Schwandt [2019, Journal of Business & Economic Statistics 37: 205–216] and Lee and Lemieux [2010, Journal of Economic Literature 48: 281–355]). While the existing commands suest (Weesie, 1999, Stata Technical Bulletin Reprints 9: 231–248) and mvreg enable Stata users to follow this approach, both are limited in several dimensions. For instance, mvreg assumes homoskedasticity and uncorrelatedness across sampling units, and neither command is designed to be used with panel data. In this article, we introduce the new community-contributed command stackreg, which overcomes the aforementioned limitations and allows for some settings and features that go beyond the capabilities of the existing commands. To achieve this, stackreg runs an ordinary least-squares regression in which the regression equations are stacked as described, for instance, in Wooldridge (2010, Econometric Analysis of Cross Section and Panel Data, p. 166–173, MIT Press) and applies cluster–robust variance–covariance estimation.