Robust data-driven inference in the regression-discontinuity design
Abstract. In this article, we introduce three commands to conduct robust data-driven
statistical inference in regression-discontinuity (RD) designs. First, we
present rdrobust, a command that implements the robust bias-corrected confidence
intervals proposed in Calonico, Cattaneo, and Titiunik (2014d, Econometrica
82: 2295–2326) for average treatment effects at the cutoff in sharp RD, sharp
kink RD, fuzzy RD, and fuzzy kink RD designs. This command also implements
other conventional nonparametric RD treatment-effect point estimators and confidence
intervals. Second, we describe the companion command rdbwselect, which
implements several bandwidth selectors proposed in the RD literature. Following
the results in Calonico, Cattaneo, and Titiunik (2014a, Working paper, University
of Michigan), we also introduce rdplot, a command that implements several
data-driven choices of the number of bins in evenly spaced and quantile-spaced
partitions that are used to construct the RD plots usually encountered in empirical
applications. A companion R package is described in Calonico, Cattaneo, and
Titiunik (2014b, Working paper, University of Michigan).
View all articles by these authors:
Sebastian Calonico, Matias D. Cattaneo, Rocío Titiunik
View all articles with these keywords:
rdrobust, rdbwselect, rdplot, regression discontinuity (RD), sharp RD, sharp kink RD, fuzzy RD, fuzzy kink RD, treatment effects, local polynomials, bias correction, bandwidth selection, RD plots
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