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The confidence intervals generated by this procedure are valid under conditions similar to the procedures proposed by Calonico et\u00a0al. (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.<\/jats:p>","DOI":"10.1093\/ectj\/utaa002","type":"journal-article","created":{"date-parts":[[2020,1,25]],"date-time":"2020-01-25T05:36:42Z","timestamp":1579930602000},"page":"211-231","source":"Crossref","is-referenced-by-count":9,"title":["Wild bootstrap for fuzzy regression discontinuity designs: obtaining robust bias-corrected confidence intervals"],"prefix":"10.1093","volume":"23","author":[{"given":"Yang","family":"He","sequence":"first","affiliation":[{"name":"Microsoft, 1 Memorial Dr., Cambridge, MA 02142, Cambridge, MA 02142"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ot\u00e1vio","family":"Bartalotti","sequence":"additional","affiliation":[{"name":"Department of Economics, Iowa State University and IZA, 468F Heady Hall, Ames, IA, 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