rcm: A command for the regression control method

Guanpeng Yan () and Qiang Chen
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Guanpeng Yan: Shandong University

Stata Journal, 2022, vol. 22, issue 4, 842-883

Abstract: The regression control method, also known as the panel-data approach for program evaluation (Hsiao, Ching, and Wan, 2012, Journal of Applied Econo- metrics 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 ex- ploits 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 co- variates. Available methods for model selection include best subset, lasso, and forward stepwise and backward stepwise regression, while available selection cri- teria 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 demon- strate 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).

Keywords: rcm; regression control method; panel-data approach; program evaluation; causal inference; counterfactual outcomes (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1177/1536867X221140960

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