Robust uniform inference for quantile treatment effects in regression discontinuity designs
Robust uniform inference for quantile treatment effects in regression discontinuity designs
复制标题
回归间断设计中分位数处理效果的稳健统一推断
DOI：
10.1016/j.jeconom.2019.03.006
复制
发表时间：
2017
影响因子：
6.3
通讯作者：
Yuya Sasaki
中科院分区：
文献类型：
--
作者：
Harold D. Chiang;Yu‐Chin Hsu;Yuya Sasaki
在回归不连续和扭结设计中，对大带宽的因果效应进行鲁棒性推理的实际重要性已得到广泛认识。现有的鲁棒方法涵盖了许多情况，但不能处理模糊设计中CDF和分位数过程的统一推理。鉴于此，本文通过开发一个统一的推理框架来扩展文献，该框架具有对大带宽的鲁棒性，适用于模糊设计中分位数处理效果的统一推理，以及所有其他情况。我们提出蒙特卡罗模拟研究和实证应用评估俄克拉何马州学前教育计划。
The practical importance of inference with robustness against large bandwidths for causal effects in regression discontinuity and kink designs is widely recognized. Existing robust methods cover many cases, but do not handle uniform inference for CDF and quantile processes in fuzzy designs. In this light, this paper extends the literature by developing a unified framework of inference with robustness against large bandwidths that applies to uniform inference for quantile treatment effects in fuzzy designs, as well as all the other cases. We present Monte Carlo simulation studies and an empirical application for evaluations of the Oklahoma pre-K program.