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Regression Discontinuity Designs Using Covariates

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

The review of economics and statistics, 2019-07, Vol.101 (3), p.442-451 [Periódico revisado por pares]

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Título:
Regression Discontinuity Designs Using Covariates
Autor: Calonico, Sebastian ; Cattaneo, Matias D. ; Farrell, Max H. ; Titiunik, Rocío
Assuntos: Bias ; Discontinuity ; Economic models ; Simulation
É parte de: The review of economics and statistics, 2019-07, Vol.101 (3), p.442-451
Notas: July, 2019
Descrição: 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 and software packages.
Editor: One Rogers Street, Cambridge, MA 02142-1209, USA: MIT Press
Idioma: Inglês

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