Spatial Correlation Robust Inference in Linear Regression and Panel Models

We consider inference about a scalar coefficient in a linear regression with spatially correlated errors. Recent suggestions for more robust inference require stationarity of both regressors and dependent variables for their large sample validity. This rules out many empirically relevant applications, such as difference-in-difference designs. We develop a robustified version of the SCPC method of Müller and Watson (2022a) that addresses this challenge. We find that the method has good size properties in a wide range of Monte Carlo designs that are calibrated to real world applications, both in a pure cross sectional setting, but also for spatially correlated panel data. We provide numerically efficient methods for computing the associated spatial-correlation robust test statistics, critical values and confidence intervals.

本文研究带有空间相关误差的线性回归模型中标量系数的统计推断问题。近期提出的更稳健推断方法，其大样本有效性要求回归元与被解释变量均满足平稳性，这一约束条件排除了诸多具有实际应用价值的场景，例如双重差分法（difference-in-difference）设计。针对这一难题，本文构建了Müller与Watson（2022a）提出的SCPC方法的稳健化版本。研究表明，在适配真实应用场景的各类蒙特卡洛（Monte Carlo）实验设计中，无论是纯横截面设定还是空间相关面板数据场景，该方法均具备优良的检验水平性质。本文还提供了用于计算该方法对应的空间相关稳健检验统计量、临界值与置信区间的数值高效方法。