Including covariates in the regression discontinuity design
Markus Frölich () and
No 489, FSES Working Papers from Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland
This paper proposes a fully nonparametric kernel method to account for observed covariates in regression discontinuity designs (RDD), which may increase precision of treatment effect estimation. It is shown that conditioning on covariates reduces the asymptotic variance and allows estimating the treatment effect at the rate of one-dimensional nonparametric regression, irrespective of the dimension of the continuously distributed elements in the conditioning set. Furthermore, the proposed method may decrease bias and restore identification by controlling for discontinuities in the covariate distribution at the discontinuity threshold, provided that all relevant discontinuously distributed variables are controlled for. To illustrate the estimation approach and its properties, we provide a simulation study and an empirical application to an Austrian labor market reform.
Keywords: Treatment effect; causal effect; complier; LATE; nonparametric regression; endogeneity (search for similar items in EconPapers)
JEL-codes: C13 C14 C21 (search for similar items in EconPapers)
Pages: 32 pages
New Economics Papers: this item is included in nep-ecm
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Journal Article: Including Covariates in the Regression Discontinuity Design (2019)
Working Paper: Including Covariates in the Regression Discontinuity Design (2017)
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