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Regression Discontinuity Designs Using Covariates

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  • Sebastian Calonico
  • Matias D. Cattaneo
  • Max H. Farrell
  • Rocio Titiunik

Abstract

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. An empirical illustration and an extensive simulation study is presented. All methods are implemented in \texttt{R} and \texttt{Stata} software packages.

Suggested Citation

  • Sebastian Calonico & Matias D. Cattaneo & Max H. Farrell & Rocio Titiunik, 2018. "Regression Discontinuity Designs Using Covariates," Papers 1809.03904, arXiv.org.
    • Handle: RePEc:arx:papers:1809.03904
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    References listed on IDEAS

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    1. Yoichi Arai & Hidehiko Ichimura, 2018. "Simultaneous selection of optimal bandwidths for the sharp regression discontinuity estimator," Quantitative Economics, Econometric Society, vol. 9(1), pages 441-482, March.
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