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Robust Data-Driven Inference in the Regression-Discontinuity Design

2014; SAGE Publishing; Volume: 14; Issue: 4 Linguagem: Inglês

10.1177/1536867x1401400413

1536-8734

Sebastián Calónico, Matias D. Cattaneo, Rocío Titiunik,

Optimal Experimental Design Methods

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).