Bootstrap Confidence Intervals for Sharp Regression Discontinuity Designs *

Regression Discontinuity Designs

ISBN: 978-1-78714-390-6, eISBN: 978-1-78714-389-0

ISSN: 0731-9053

Publication date: 13 May 2017

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

Citation

Bartalotti, O., Calhoun, G. and He, Y. (2017), "Bootstrap Confidence Intervals for Sharp Regression Discontinuity Designs * ", Regression Discontinuity Designs (Advances in Econometrics, Vol. 38), Emerald Publishing Limited, pp. 421-453. https://doi.org/10.1108/S0731-905320170000038018

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Emerald Publishing Limited

Copyright © 2017 Emerald Publishing Limited

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