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Article
Wild bootstrap for fuzzy regression discontinuity designs: obtaining robust bias-corrected confidence intervals
The Econometrics Journal
Document Type
Article
Disciplines
Publication Version
Submitted Manuscript
Publication Date
5-1-2020
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.
Copyright Owner
Royal Economic Society
Copyright Date
2020
Language
en
File Format
application/pdf
Citation Information
Yang He and Otávio Bartalotti. "Wild bootstrap for fuzzy regression discontinuity designs: obtaining robust bias-corrected confidence intervals" The Econometrics Journal Vol. 23 Iss. 2 (2020) p. 211 - 231 Available at: http://works.bepress.com/otavio-bartalotti/16/
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.