Spatial Growth Regressions: Model Specification, Estimation and Interpretation
James LeSage () and
Spatial Economic Analysis, 2008, vol. 3, issue 3, 275-304
Abstract We attempt to clarify a number of points regarding use of spatial regression models for regional growth analysis. We show that as in the case of non-spatial growth regressions, the effect of initial regional income levels wears off over time. Unlike the non-spatial case, long-run regional income levels depend on: own region as well as neighbouring region characteristics, the spatial connectivity structure of the regions, and the strength of spatial dependence. Given this, the search for regional characteristics that exert important influences on income levels or growth rates should take place using spatial econometric methods that account for spatial dependence as well as own and neighbouring region characteristics, the type of spatial regression model specification, and weight matrix. The framework adopted here illustrates a unified approach for dealing with these issues.
Keywords: Model uncertainty; Bayesian model averaging; Markov chain Monte Carlo model composition; spatial weight structures; C11; C21; 047; 052; R11 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:taf:specan:v:3:y:2008:i:3:p:275-304
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