Regression Discontinuity Designs Using Covariates

• 成果类型: Article
• 署名作者: Calonico, Sebastian; Cattaneo, Matias D.; Farrell, Max H.; Titiunik, Rocio
• 署名单位: Columbia University; Princeton University; University of Chicago
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_00760
• 发表日期: 2019-07
• 页码: 442-451
• 关键词: head-start; adjustments; inference; selection; guide

摘要

We study regression discontinuity designs when covariates are included in the estimation. We examine local polynomial estimators that include discrete or continuous covariates in an additive separable way, but without imposing any parametric restrictions on the underlying population regression functions. We recommend a covariate-adjustment approach that retains consistency under intuitive conditions and characterize the potential for estimation and inference improvements. We also present new covariate-adjusted mean-squared error expansions and robust bias-corrected inference procedures, with heteroskedasticity-consistent and cluster-robust standard errors. We provide an empirical illustration and an extensive simulation study. All methods are implemented in R and Stata software packages.