Title

Bootstrap Confidence Intervals for Sharp Regression Discontinuity Designs

Campus Units

Economics

Document Type

Book Chapter

Publication Version

Submitted Manuscript

Publication Date

2017

Journal or Book Title

Regression Discontinuity Designs: Theory and Applications

Editors

Matias D. Cattaneo, Juan Carlos Escanciano

Publisher

Emerald Publishing Limited

Place of Publication

United Kingdom

First Page or Article ID Number

421

Last Page

453

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.

JEL Classification

C14, C15, C21

Comments

This is a working paper of a chapter from Otávio Bartalotti, Gray Calhoun, Yang He, (2017), Bootstrap Confidence Intervals for Sharp Regression Discontinuity Designs, in Matias D. Cattaneo and Juan Carlos Escanciano (ed.) Regression Discontinuity Designs (Advances in Econometrics, Volume 38) Emerald Publishing Limited, pp.421 - 453. Posted with permission.

Copyright Owner

Emerald Publishing Limited

Language

en

File Format

application/pdf

Published Version

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