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Spatial Correlation Robust Inference
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DOI：10.3982/ECTA19465.png)
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Abstract
En 中文
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.
Keywords:
Confidence interval
HAR
HAC
random field