Inference in difference‐in‐differences: How much should we trust in independent clusters?
分析了双重差分方法中存在空间相关性时的推断问题，指出估计的时间框架、处理组与对照组选择等因素会影响空间相关性导致的偏差，并为应用研究者提供了缓解和评估推断扭曲的建议。
Summary We analyze the challenges for inference in difference‐in‐differences (DID) when there is spatial correlation. We present novel theoretical insights and empirical evidence on the settings in which ignoring spatial correlation should lead to more or less distortions in DID applications. We show that details, such as the time frame used in the estimation, the choice of the treated and control groups, and the choice of the estimator, are key determinants of distortions due to spatial correlation. We also analyze the feasibility and trade‐offs involved in a series of alternatives to take spatial correlation into account. Given that, we provide relevant recommendations for applied researchers on how to mitigate and assess the possibility of inference distortions due to spatial correlation.