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Robust data-driven inference in the regression-discontinuity design

Author

Listed:
  • Sebastian Calonico

    (University of Miami)

  • Matias D. Cattaneo

    (University of Michigan)

  • Rocio Titiunik

    (University of Michigan)

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.

Suggested Citation

  • Sebastian Calonico & Matias D. Cattaneo & Rocio Titiunik, 2014. "Robust data-driven inference in the regression-discontinuity design," Stata Journal, StataCorp LP, vol. 14(4), pages 909-946, December.
  • Handle: RePEc:tsj:stataj:v:14:y:2014:i:4:p:909-946
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