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

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

Suggested Citation

  • He, Yang & Bartalotti, Otávio, 2019. "Wild bootstrap for fuzzy regression discontinuity designs: obtaining robust bias-corrected confidence intervals," ISU General Staff Papers 201903010800001071, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genstf:201903010800001071
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    Cited by:

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    4. Chen, Yen-Chien & Fan, Elliott & Ho, Yu-Hsin & Lee, Matthew Yi-Hsiu & Liu, Jin-Tan, 2023. "How Does Gender Quota Shape Gender Attitudes?," IZA Discussion Papers 16331, Institute of Labor Economics (IZA).
    5. Ellegård, Lina Maria & Kjellsson, Gustav & Mattisson, Linn, 2021. "An App Call a Day Keeps the Patient Away? Substitution of Online and In-Person Doctor Consultations Among Young Adults," Working Papers in Economics 808, University of Gothenburg, Department of Economics, revised May 2022.

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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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