Regression discontinuity designs using covariates

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

Research output: Contribution to journalArticle

5 Citations (Scopus)

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

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regression
simulation
trend
Regression discontinuity design
Inference
Covariates
software
Software
Heteroskedasticity
Covariate adjustment
Mean squared error
Simulation study
Estimator
Local polynomial
Standard error

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Economics and Econometrics

Cite this

Regression discontinuity designs using covariates. / Calonico, Sebastian; Cattaneo, Matias D.; Farrell, Max H.; Titiunik, Rocío.

In: Review of Economics and Statistics, Vol. 101, No. 3, 01.07.2019, p. 442-451.

Research output: Contribution to journalArticle

Calonico, Sebastian ; Cattaneo, Matias D. ; Farrell, Max H. ; Titiunik, Rocío. / Regression discontinuity designs using covariates. In: Review of Economics and Statistics. 2019 ; Vol. 101, No. 3. pp. 442-451.
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