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  -