In this paper, we outline a heuristic to determine the spatial regimes endogenously, as an extension of the well-known SKATER algorithm for spatially constrained clustering. One drawback of regime delineation is that the results do not necessarily satisfy a spatial contiguity constraint, i.e., observations are grouped despite not being spatially connected. ![]() A number of different methods have been suggested in the literature, including finite mixture models, GWR-based methods, and penalized regression. Tightly integrated approaches are referred to as endogenous spatial regimes. Generally speaking, two broad classes of methods can be distinguished, one in which the delineation is carried out separately from the coefficient estimation and one where the two are tightly integrated. Whereas the estimation of spatial regime regressions is well understood, the delineation of the regimes themselves remains a topic of active interest. ![]() In a discrete perspective, referred to as spatial regimes, the coefficients vary by discrete subregions of the data. This can be approached from a continuous or a discrete perspective. The pioneering work of Getis and Ord on local spatial statistics has a counterpart in spatial econometrics in treating spatial heterogeneity.
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