Wild Bootstrap for Fuzzy Regression Discontinuity Designs: Obtaining Robust Bias-Corrected Confidence Intervals

Yang He () and Otavio Bartalotti
Additional contact information
Yang He: Microsoft Corporation

No 12801, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: This paper develops a novel wild bootstrap procedure to construct robust bias-corrected (RBC) valid confidence intervals (CIs) for fuzzy regression discontinuity designs, providing an intuitive complement to existing RBC methods. The CIs 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.

Keywords: wild bootstrap; robust confidence intervals; fuzzy regression discontinuity; average treatment effect (search for similar items in EconPapers)
JEL-codes: C14 C21 C26 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2019-11
New Economics Papers: this item is included in nep-ecm and nep-ore
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Published - published in: Econometrics Journal, 2020, 23 (2), 211–231

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https://docs.iza.org/dp12801.pdf (application/pdf)

Related works:
Journal Article: Wild bootstrap for fuzzy regression discontinuity designs: obtaining robust bias-corrected confidence intervals (2020)
Working Paper: Wild bootstrap for fuzzy regression discontinuity designs: obtaining robust bias-corrected confidence intervals (2020)
Working Paper: Wild bootstrap for fuzzy regression discontinuity designs: obtaining robust bias-corrected confidence intervals (2019)
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