Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/215197 
Year of Publication: 
2019
Series/Report no.: 
IZA Discussion Papers No. 12801
Publisher: 
Institute of Labor Economics (IZA), Bonn
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.
Subjects: 
fuzzy regression discontinuity
robust confidence intervals
wild bootstrap
average treatment effect
JEL: 
C14
C21
C26
Document Type: 
Working Paper

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