Title

Spatial Correlation Robust Inference

Author(s)

Ulrich K. Müller Ulrich Müller (Princeton University)

Mark W. Watson Mark Watson (Princeton University)

Abstract

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 covariance 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 large samples whenever the spatial correlation is weak, i.e., with average pairwise correlations that vanish as the sample size gets large. We also provide results on correct coverage in a restricted but nonparametric class of strong spatial correlations, as well as on the efficiency of the method. In a design calibrated to match economic activity in U.S. states the method outperforms previous suggestions for spatially robust inference about the population mean.

Creation Date

2021-11

Section URL ID

Paper Number

2021-61

URL

http://www.princeton.edu/~umueller/SHAR.pdf

File Function

Jel

C12, C20

Keyword(s)

Confidence interval, HAR, HAC, Random field

Suppress

false

Series

13