American Economic Review · 2021 · Dmitry Arkhangelsky、Susan Carleton Athey、David A. Hirshberg、Guido W. Imbens、Stefan Wager
中文摘要
我们提出了一种用于面板数据因果效应的新估计量，它建立在被广泛使用的双重差分与合成控制方法背后的洞见之上。相对于这些方法，我们从理论和实证上均发现，这种“合成双重差分”估计量具有理想的稳健性，并且在实践中通常使用常规估计量的设定下表现良好。当结果模型的系统部分包含潜在单元因子与潜在时间因子的交互项时，我们研究了该估计量的渐近行为，并给出了其一致性与渐近正态性的条件。（JEL C23, H25, H71, I18, L66）
Abstract
We present a new estimator for causal effects with panel data that builds on insights behind the widely used difference-in-differences and synthetic control methods. Relative to these methods we find, both theoretically and empirically, that this “synthetic difference-in-differences” estimator has desirable robustness properties, and that it performs well in settings where the conventional estimators are commonly used in practice. We study the asymptotic behavior of the estimator when the systematic part of the outcome model includes latent unit factors interacted with latent time factors, and we present conditions for consistency and asymptotic normality. (JEL C23, H25, H71, I18, L66)