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Regression discontinuity designs using covariates

Author(s): Calonico, S; Cattaneo, Mattias D; Farrell, MH; Titiunik, R

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Abstract: © 2019 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology. —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.
Publication Date: 1-Jul-2019
Citation: Calonico, S, Cattaneo, MD, Farrell, MH, Titiunik, R. (2019). Regression discontinuity designs using covariates. Review of Economics and Statistics, 101 (3), 442 - 451. doi:10.1162/rest_a_00760
DOI: doi:10.1162/rest_a_00760
ISSN: 0034-6535
EISSN: 1530-9142
Pages: 442 - 451
Type of Material: Journal Article
Journal/Proceeding Title: Review of Economics and Statistics
Version: Author's manuscript

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