This 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.

中文翻译：

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模糊回归不连续性设计的野生引导程序：获得鲁棒的偏差校正后的置信区间

本文开发了一种新颖的野生自举程序，可为模糊回归不连续性设计构建鲁棒的，经偏置校正的有效置信区间，为现有的鲁棒的经偏置校正的方法提供直观的补充。该程序生成的置信区间在与Calonico等人建议的程序类似的条件下有效。（2014年）及相关文献。仿真提供了证据，证明该新方法在应用于具有内生性和聚类性的各种数据生成过程时，其准确性至少与插件分析校正一样。最后，我们通过回顾对学生成绩的班级规模的Angrist和Lavy（1999）的分析来证明其经验相关性。