分布双重差分：分位数反转前聚合还是后聚合？
Distributional Difference-in-Differences: Aggregation Before or After Quantile Inversion?
- University at Albany - State University of New York（纽约州立大学奥尔巴尼分校）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
研究交错分布双重差分中分位数处理效应的两种聚合方式，证明聚合顺序影响估计目标与符号，需在反转前确定。
中文摘要 AI 辅助
交错分布双重差分产生队列特定的潜在结果分布，但应用工作通常希望得到一个总体分位数处理效应。两种自然的汇总方式——对队列分位数处理效应（QTTs）取平均，以及在反转前混合队列分布——使用相同的政策权重，却回答不同的目标总体问题，甚至可能在符号上产生分歧。我们推导了在给定队列分位数和权重条件下，两者差距的精确锐区间、全局锐范围包络以及局部锐密度倾斜表示。我们开发了联合平滑且对质量点安全的推断方法，并证明即使估计目标一致，两种估计量在精度上也没有统一优势。在公共交错QTT应用的同对象重构中，保持数据、识别、分布和权重不变，仅改变聚合顺序，就在多个分位数上反转了报告符号。因此，聚合顺序是估计目标的一部分，必须在反转之前选择。
英文摘要
Staggered distributional difference-in-differences produces cohort-specific potential-outcome distributions, but applied work typically wants one overall quantile treatment effect. Two natural summaries---averaging cohort quantile treatment effects (QTTs) and mixing cohort distributions before inversion---use the same policy weights yet answer different target-population questions and can disagree even in sign. We derive the exact sharp interval for their gap conditional on cohort quantiles and weights, a globally sharp range-only envelope, and a locally sharp density-tilt representation. We develop joint smooth and mass-point-safe inference and show that neither estimator is uniformly more precise, even when the estimands coincide. In a same-object reconstruction of a public staggered-QTT application, holding data, identification, distributions, and weights fixed while changing only aggregation order reverses reported signs at several quantiles. Aggregation order is therefore part of the estimand and must be chosen before inversion.
