Spatial Correlation Robust Inference

• 成果类型: Article
• 署名作者: Muller, Ulrich K.; Watson, Mark W.
• 署名单位: Princeton University
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA19465
• 发表日期: 2022
• 页码: 2901-2935
• 关键词: Asymptotic Theory; HAC ESTIMATION; heteroskedasticity

摘要

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.