Bootstrap Confidence Intervals for Sharp Regression Discontinuity Designs with the Uniform Kernel
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This paper develops a novel bootstrap procedure to obtain robust bias-corrected confidence intervals in regression discontinuity (RD) designs using the uniform kernel. The procedure uses a residual 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. The confidence intervals generated by this procedure are valid under conditions similar to Calonico, Cattaneo and Titiunik's (2014, Econometrica) analytical correction---i.e. when the bias of the naive regression discontinuity estimator would otherwise prevent valid inference.
This paper 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 (2008) Head Start dataset.