Regression discontinuity designs using covariates

Sebastian Calonico, Matias D. Cattaneo, Max H. Farrell, Rocío Titiunik

Research output: Contribution to journalArticle

32 Scopus citations

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 covariateadjusted 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.

Original languageEnglish (US)
Pages (from-to)442-451
Number of pages10
JournalReview of Economics and Statistics
Volume101
Issue number3
DOIs
StatePublished - Jul 1 2019
Externally publishedYes

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Economics and Econometrics

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