Modeling spatial variations in household disposable income with Geographically Weighted Regression
The purpose of this paper is to analyze the spatially varying impacts of some classical regressors on per capita household income in Spanish provinces. The authors model this distribution following both a traditional global regression and a local analysis with Geographically Weighted Regression (GWR). Several specifications are compared, being the adaptive bisquare weighting function the more efficient in terms of goodness-of-fit. We test for global and local spatial instability using some F-tests and other statistical measures. We find some evidence of spatial instability in the distribution of this variable in relation to some explanatory variables, which cannot be totally solved by spatial dependence specifications. GWR has revealed as a better specification to model per capita household income. It highlights some facets of the relationship completely hidden in the global results and forces us to ask about questions we would otherwise not have asked. Moreover, the application of GWR can also be of help to further exercises of micro-data spatial prediction.
|Date of creation:||24 Jan 2007|
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- Griffith, Daniel A., 1992. "A spatially adjusted N-way ANOVA model," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 347-369, September.
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