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.
The authors would like to thank Matias Cattaneo, Juan Carlos Escanciano, Sebastian Calonico, Max Farrell, Helle Bunzel, Brent Kreider, Quentin Brummet, seminar participants at the Advances in Econometrics conference on Regression Discontinuity Designs, Third Annual Conference of the International Association for Applied Econometrics, 2016 North American Summer Meeting of the Econometric Society and the 69th European Meeting of the Econometric Society, and especially the two anonymous referees for substantial help and advice on earlier versions of this chapter.
Bartalotti, O., Calhoun, G. and He, Y. (2017), "Bootstrap Confidence Intervals for Sharp Regression Discontinuity Designs
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