发表机构
London School of Economics（伦敦政治经济学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对重复发生的持久性处理事件，提出基于条件平行趋势假设的顺序插补估计器，以一致估计每次事件的动态效应并容纳异质性，优于传统TWFE模型。
AI 中文摘要
我研究了当处理事件具有持续效应且可能经历多次时的处理效应估计问题。自然灾害、失业和健康冲击是此类处理的例子。我表明，在类似于单事件设置中通常引用的假设下，使用适当灵活的TWFE模型，可以恢复总处理轨迹的效应。然而，将总轨迹效应分解为可归因于不同事件发生的部分，则需要进一步的假设。我提出了一个类似于条件平行趋势的假设，将其施加于事件特定效应的增长而非未处理结果上。结合效应增长的线性参数模型，该假设使得一种顺序插补估计器能够一致地估计每次事件发生的动态效应，并能根据可观测的事件属性（如强度）容纳效应的异质性。我证明了几个直观的TWFE模型在多事件设置中无法恢复可解释的处理效应参数，并通过蒙特卡洛模拟展示了顺序插补估计器的优越性能。
英文摘要
I study treatment effect estimation when treatment events have persistent effects and can be experienced more than once. Natural disasters, job loss and health shocks are examples of such treatments. I show that the effect of a total treatment trajectory can be recovered under assumptions similar to those commonly invoked in single-event settings using suitably flexible TWFE models. Decomposing the total trajectory effect into portions attributable to distinct event occurrences, however, requires further assumptions. I propose an assumption similar to conditional parallel trends, imposing it on the growth of event-specific effects rather than on untreated outcomes. Combined with a linear-in-parameters model of effect growth, this assumption enables a sequential imputation estimator that consistently estimates the dynamic effects of each event occurrence and that can accommodate heterogeneity in effects according to observable event attributes, such as intensity. I demonstrate that several intuitive TWFE models fail to recover interpretable treatment effect parameters in the multi-event setting and illustrate the sequential imputation estimator's favourable performance using Monte Carlo simulations.
