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Measurement Error and the Specification of the Weights Matrix in Spatial Regression Models

Garrett N. Vande Kamp

Political Analysis, 2020, vol. 28, issue 2, 284-292

Abstract: While the spatial weights matrix $\boldsymbol{W}$ is at the core of spatial regression models, there is a scarcity of techniques for validating a given specification of $\boldsymbol{W}$. I approach this problem from a measurement error perspective. When $\boldsymbol{W}$ is inflated by a constant, a predictable form of endogeneity occurs that is not problematic in other regression contexts. I use this insight to construct a theoretically appealing test and control for the validity of $\boldsymbol{W}$ that is tractable in panel data, which I call the K test. I demonstrate the utility of the test using Monte Carlo simulations.

Date: 2020
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Handle: RePEc:cup:polals:v:28:y:2020:i:2:p:284-292_8