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

Yang He and Otavio Bartalotti

The Econometrics Journal, 2020, vol. 23, issue 2, 211-231

Abstract: SummaryThis 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.

Keywords: Fuzzy regression discontinuity; robust confidence intervals; wild bootstrap; average treatment effect (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)

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Related works:
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)
Working Paper: Wild Bootstrap for Fuzzy Regression Discontinuity Designs: Obtaining Robust Bias-Corrected Confidence Intervals (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:oup:emjrnl:v:23:y:2020:i:2:p:211-231.

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