TY - CHAP AB - 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. VL - 38 SN - 978-1-78714-390-6, 978-1-78714-389-0/0731-9053 DO - 10.1108/S0731-905320170000038018 UR - https://doi.org/10.1108/S0731-905320170000038018 AU - Bartalotti Otávio AU - Calhoun Gray AU - He Yang PY - 2017 Y1 - 2017/01/01 TI - Bootstrap Confidence Intervals for Sharp Regression Discontinuity Designs* T2 - Regression Discontinuity Designs T3 - Advances in Econometrics PB - Emerald Publishing Limited SP - 421 EP - 453 Y2 - 2024/09/19 ER -