重新审视事件研究设计：稳健且高效的估计
Revisiting Event Study Designs: Robust and Efficient Estimation
AI总结：
本文针对交错处理采纳和异质性处理效应下的双重差分设计，提出了一种稳健且高效的插补形式估计量，并配套推断与识别检验工具，通过模拟和美国税收返还消费响应应用验证了其有效性。
摘要：
我们为具有交错处理采纳和异质性因果效应的双重差分设计开发了一个框架。我们表明，在缺乏对处理效应同质性的强限制条件下，传统的基于回归的估计量无法提供相关估计目标的无偏估计。随后，我们推导出应对这一挑战的高效估计量，当处理效应异质性不受限制时，该估计量呈现出直观的“插补”形式。我们刻画了该估计量的渐近行为，提出了推断工具，并开发了用于检验识别假设的检验方法。我们的方法适用于时变控制变量、三重差分设计以及某些非二元处理的情形。我们通过一项模拟研究和一项应用展示了研究结果的实际相关性。在研究美国税收返还对消费的响应时，我们发现名义边际消费倾向在第一季度介于8%至11%之间——大约只有用于校准宏观经济模型的基准估计值的一半——并且主要发生在返还后的第一个月内。
英文摘要：
We develop a framework for difference-in-differences designs with staggered treatment adoption and heterogeneous causal effects. We show that conventional regression-based estimators fail to provide unbiased estimates of relevant estimands absent strong restrictions on treatment-effect homogeneity. We then derive the efficient estimator addressing this challenge, which takes an intuitive "imputation" form when treatment-effect heterogeneity is unrestricted. We characterize the asymptotic behavior of the estimator, propose tools for inference, and develop tests for identifying assumptions. Our method applies with time-varying controls, in triple-difference designs, and with certain non-binary treatments. We show the practical relevance of our results in a simulation study and an application. Studying the consumption response to tax rebates in the United States, we find that the notional marginal propensity to consume is between 8 and 11 percent in the first quarter - about half as large as benchmark estimates used to calibrate macroeconomic models - and predominantly occurs in the first month after the rebate.
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