中文摘要
我们构建了一个适用于交错（分批）实施处理且因果效应具有异质性的双重差分设计框架。我们表明，若不对处理效应同质性施加强限制，传统基于回归的估计量无法对相关估计目标给出无偏估计。随后，我们推导出应对这一挑战的有效估计量；当处理效应异质性不受限制时，该估计量具有直观的“插补”形式。我们刻画了该估计量的渐近性质，提出了推断工具，并开发了针对识别假设的检验方法。我们的方法适用于包含时变控制变量的情形、三重差分设计以及某些非二元处理。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意义。在研究美国的退税消费反应时，我们发现，第一季度的名义边际消费倾向介于8%至11%之间，约为校准宏观经济模型所用基准估计值的一半，且消费反应主要发生在退税后的第一个月。
Abstract
Abstract 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 behaviour 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 U.S., we find that the notional marginal propensity to consume is between 8 and 11% 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.