Title

Wild bootstrap for fuzzy regression discontinuity designs: obtaining robust bias-corrected confidence intervals

Campus Units

Economics

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

5-2020

Journal or Book Title

The Econometrics Journal

Volume

23

Issue

2

First Page or Article ID Number

211

Last Page

231

DOI

10.1093/ectj/utaa002

Abstract

This paper develops a novel wild bootstrap procedure to construct robust bias-corrected valid confidence intervals for fuzzy regression discontinuity designs, providing an intuitive complement to existing robust bias-corrected methods. The confidence intervals generated by this procedure are valid under conditions similar to the procedures proposed by Calonico et al. (2014) and related literature. Simulations provide evidence that this new method is at least as accurate as the plug-in analytical corrections when applied to a variety of data-generating processes featuring endogeneity and clustering. Finally, we demonstrate its empirical relevance by revisiting Angrist and Lavy (1999) analysis of class size on student outcomes.

JEL Classification

C01, C14, C21

Comments

This is a working paper of an article published as He, Yang, and Otávio Bartalotti. "Wild bootstrap for fuzzy regression discontinuity designs: obtaining robust bias-corrected confidence intervals." The Econometrics Journal 23, no. 2 (2020): 211-231. doi: 10.1093/ectj/utaa002. Posted with permission.

Copyright Owner

Royal Economic Society

Language

en

File Format

application/pdf

Published Version

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