Spatial Correlation Robust Inference
提出一种构建置信区间的方法，能处理多种空间相关性，通过新的标准误和临界值构造方式，在有限样本和高斯设定下控制覆盖概率，适用于空间相关性较弱的大样本场景。
We propose a method for constructing confidence intervals that account for many forms of spatial correlation. The interval has the familiar “estimator plus and minus a standard error times a critical value” form, but we propose new methods for constructing the standard error and the critical value. The standard error is constructed using population principal components from a given “worst‐case” spatial correlation model. The critical value is chosen to ensure coverage in a benchmark parametric model for the spatial correlations. The method is shown to control coverage in finite sample Gaussian settings in a restricted but nonparametric class of models and in large samples whenever the spatial correlation is weak, that is, with average pairwise correlations that vanish as the sample size gets large. We also provide results on the efficiency of the method.