While the spatial weights matrix W is at the core of spatial regression models, there is a scarcity of techniques for validating a given specification of W. I approach this problem from a measurement error perspective. When 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 W that is tractable in panel data, which I call the K test. I demonstrate the utility of the test using Monte Carlo simulations.
Vande Kamp, G. N. (2020). Measurement Error and the Specification of the Weights Matrix in Spatial Regression Models. Political Analysis, 28(2), 284–292. doi:10.1017/pan.2019.35
