Testing coefficient stability in spatial regression

This paper develops a test for coefficient variability in spatial regressions. The test is designed to have good power for a wide range of persistent patterns of coefficient variation, be applicable in a wide range of spatial designs, and accommodate spatial correlation in regressors and regression errors. The test approximates the locally best invariant test for coefficient stability in a Gaussian regression model with martingale-like coefficient variation under the alternative, and is thus a spatial generalization of the Nyblom (1989) test of coefficient stability in time series regressions.

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