Many social events and policy interventions generate treatment effects that persistently spill over into neighboring areas, resulting in a phenomenon statisticians refer to as "interference" both in time and space. In this paper, I put forward a design-based framework to identify and estimate these spillover effects in panel data with a spatial dimension, when temporal and spatial interference intertwine in intricate ways that are unknown to researchers. The framework defines estimands that enable researchers to measure the influence of each type of interference, and I propose estimators that are consistent and asymptotically normal under the assumption of sequential ignorability and mild regularity conditions. I show that fixed effects models in panel data analysis, such as the difference-in-differences (DID) estimator, can lead to significant biases in such scenarios. I test the method's performance on both simulated datasets and the replication of two empirical studies.

翻译：许多社会事件和政策干预会产生持续溢出至邻近区域的处置效应，从而在时间与空间维度上形成统计学家所称的"干扰"现象。本文提出一个基于设计的分析框架，用于在时空干扰以研究者未知的复杂方式交织时，识别并估计具有空间维度的面板数据中的这些溢出效应。该框架定义了能够衡量各类干扰影响的目标量，并在序列可忽略性假设和温和正则条件下，提出了一致且渐近正态的估计量。研究表明，面板数据分析中的固定效应模型（如双重差分估计量）在此类情境下可能导致显著偏差。本文通过模拟数据集以及对两项实证研究的复现验证了该方法的表现。