rcm: A command for the regression control method

Abstract

The regression control method, also known as the panel-data approach for program evaluation (Hsiao, Ching, and Wan, 2012, Journal of Applied Econometrics 27: 705–740; Hsiao and Zhou, 2019, Journal of Applied Econometrics 34: 463–481), is a convenient method for causal inference in panel data that exploits cross-sectional correlation to construct counterfactual outcomes for a single treated unit by linear regression. In this article, we present the rcm command, which efficiently implements the regression control method with or without covariates. Available methods for model selection include best subset, lasso, and forward stepwise and backward stepwise regression, while available selection criteria include the corrected Akaike information criterion, the Akaike information criterion, the Bayesian information criterion, the modified Bayesian information criterion, and cross-validation. Estimation and counterfactual predictions can be made by ordinary least squares, lasso, or postlasso ordinary least squares. For statistical inference, both the in-space placebo test using fake treatment units and the in-time placebo test using a fake treatment time can be implemented. The rcm command produces a series of graphs for visualization along the way. We demonstrate the use of the rcm command by revisiting classic examples of political and economic integration between Hong Kong and mainland China (Hsiao, Ching, and Wan 2012) and German reunification (Abadie, Diamond, and Hainmueller, 2015, American Journal of Political Science 59: 495–510).