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Abstract
Spatial regression models incorporating non-stationarity in the regression coefficients are popular. We propose a spatial variant of the Smooth Transition AutoRegressive (STAR) model that is more parsimonious than commonly used approaches and endogenously determines the extent of spatial parameter variation. Uncomplicated estimation and inference procedures are demonstrated using a neoclassical convergence model for United States counties.