发表机构
University College London（伦敦大学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种基于核函数的双重稳健估计方法，利用预处理结果历史控制潜在异质性，识别交错采纳下的动态处理效应，并应用于美国计划生育项目，估计其对生育率的影响。
AI 中文摘要
我研究了交错采纳情形下面板数据中的动态处理效应，其中处理时点同时依赖于未观测的时不变异质性和时变预处理协变量（包括滞后结果）。未处理潜在结果遵循一个非参数动态面板模型，该模型允许时变协变量与潜在异质性之间存在灵活交互。我利用预处理结果历史来寻找具有相似时不变潜在因子的个体，关键要求是这些历史信息足以充分反映那些潜在因子。我针对处理组的动态平均处理效应（ATT）提出了一种识别策略，并提出了基于核函数的双重稳健估计量来估计动态ATT。我进一步将双重交叉拟合与欠平滑相结合，并证明在适当的正则条件下，所提出的估计量具有$\sqrt{n}$一致性、渐近正态性和渐近无偏性。模拟研究表明，所提出的方法在广泛的数据生成过程中提供了准确的推断。我将该方法应用于Bailey（2012）研究的美国计划生育项目，并重新估计了其对生育率的影响。
英文摘要
I study dynamic treatment effects in panel data under staggered adoption when treatment timing depends jointly on unobserved time-invariant heterogeneity and time-varying pretreatment covariates, including lagged outcomes. Untreated potential outcomes follow a nonparametric dynamic panel model that allows flexible interactions between time-varying covariates and latent heterogeneity. I use pretreatment outcome histories to find individuals with similar time-invariant latent factors, and the key requirement is that these histories are sufficiently informative about those latent factors. I develop an identification strategy for the dynamic average treatment effect on the treated (ATT) and propose kernel-based doubly robust estimators for the dynamic ATT. I further combine double cross-fitting with undersmoothing and show that, under suitable regularity conditions, the proposed estimators are $\sqrt{n}$-consistent, asymptotically normal, and asymptotically unbiased. The simulation study demonstrates that the proposed method provides accurate inference across a wide range of data-generating processes. I illustrate the method with an application to the U.S. family planning program studied by Bailey (2012) and reestimate its effect on fertility rates.
