TY  - RPRT
        AU  - He, Yang
AU  - Bartalotti, Otávio
        TI  - Wild Bootstrap for Fuzzy Regression Discontinuity Designs: Obtaining Robust Bias-Corrected Confidence Intervals
        PY  - 2019/Nov/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 12801
        UR  - https://www.iza.org/publications/dp12801
        AB  - 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. 
        KW  - wild bootstrap
KW  - robust confidence intervals
KW  - fuzzy regression discontinuity
KW  - average treatment effect
        ER  -