回归不连续设计中的异质性处理效应检验
Testing for Heterogeneous Treatment Effects in Regression Discontinuity Designs
- Guanghua School of Management, Peking University（北京大学光华管理学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种非参数检验，用于检测回归不连续设计中的未观测处理效应异质性，通过特征函数转化为积分条件矩约束，并验证了检验的渐近性质与有效性。
AI中文摘要：
我们提出了一种非参数检验方法，用于检测回归不连续设计中未观测到的处理效应异质性。在无未观测异质性的原假设下，一种将处理组的潜在结果归因于未处理组的转换结果，在断点处必须具有连续的条件分布。我们利用特征函数将此含义转化为积分条件矩约束，从而允许条件局部平均处理效应成为协变量的无约束函数。我们通过$U$-过程推导了检验统计量的渐近分布，并建立了用于计算临界值的乘子自助法的有效性。蒙特卡洛实验显示，检验的规模控制良好且功效递增。两个实证应用说明了该检验如何区分由可观测变量解释的异质性与由未观测因素解释的异质性。
英文摘要：
We propose a nonparametric test for unobserved treatment effect heterogeneity in regression discontinuity designs. Under the null of no unobserved heterogeneity, a transformed outcome that imputes treated potential outcomes for untreated units must have a continuous conditional distribution at the cutoff. We convert this implication into an integrated conditional-moment restriction using characteristic functions, thereby allowing the conditional local average treatment effect to be an unrestricted function of covariates. We derive the asymptotic distribution of the test statistics via a $U$-process and establish the validity of a multiplier bootstrap procedure for calculating critical values. Monte Carlo experiments show well-controlled size and increasing power. Two empirical applications illustrate how the test distinguishes between heterogeneity explained by observables and that explained by unobserved factors.
