Bootstrap Confidence Intervals for Sharp Regression Discontinuity Designs*

Otavio Bartalotti, Gray Calhoun and Yang He

A chapter in Regression Discontinuity Designs, 2017, vol. 38, pp 421-453 from Emerald Group Publishing Limited

Abstract: This chapter develops a novel bootstrap procedure to obtain robust bias-corrected confidence intervals in regression discontinuity (RD) designs. The procedure uses a wild bootstrap from a second-order local polynomial to estimate the bias of the local linear RD estimator; the bias is then subtracted from the original estimator. The bias-corrected estimator is then bootstrapped itself to generate valid confidence intervals (CIs). The CIs generated by this procedure are valid under conditions similar to Calonico, Cattaneo, and Titiunik’s (2014) analytical correction – that is, when the bias of the naive RD estimator would otherwise prevent valid inference. This chapter also provides simulation evidence that our method is as accurate as the analytical corrections and we demonstrate its use through a reanalysis of Ludwig and Miller’s (2007) Head Start dataset.

Keywords: Regression discontinuity; bias correction; wild bootstrap; iterated bootstrap; C14; C15; C21 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (6)

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Working Paper: Bootstrap Confidence Intervals for Sharp Regression Discontinuity Designs (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-905320170000038018

DOI: 10.1108/S0731-905320170000038018

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