Robust data-driven inference in the regression-discontinuity design

Sebastian Calonico, Matias Cattaneo and Rocio Titiunik

Stata Journal, 2014, vol. 14, issue 4, 909-946

Abstract: In this article, we introduce three commands to conduct robust datadriven 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). Copyright 2014 by StataCorp LP.

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 (search for similar items in EconPapers)
Date: 2014
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