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On spatial econometric models, spillover effects, and W

Author

Listed:
  • Paul Elhorst

  • Solmaria Halleck Vega

Abstract

Spatial econometrics has recently been appraised in a theme issue of the Journal of Regional Science. Partridge et al. (2012) provide an overview of the three contributing papers, the most critical being Gibbons and Overman (2012). Although some of the critiques raised are valid, they are issues that can be overcome by improving applied spatial econometric work. There has been excessive use of so-called global spillover models and too much emphasis on statistical testing procedures. Theory or the specific context of the empirical application should be the main guide for specifying a model. Especially the so-called SLX model merits more attention, as it produces local spillovers that are different for each explanatory variable in the equation and it allows for the parameterization of the spatial weights matrix W. The latter is a key contribution since a major concern of spatial econometric modeling, as is thoroughly discussed in Corrado and Fingleton (2012), is the a priori specification of W. This paper highlights these issues with an empirical application and recently proposed approaches for selecting a model specification, which are useful and promising steps forward for applied work involving spatial econometrics.

Suggested Citation

  • Paul Elhorst & Solmaria Halleck Vega, 2013. "On spatial econometric models, spillover effects, and W," ERSA conference papers ersa13p222, European Regional Science Association.
    • Handle: RePEc:wiw:wiwrsa:ersa13p222
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    File URL: https://www-sre.wu.ac.at/ersa/ersaconfs/ersa13/ERSA2013_paper_00222.pdf
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    References listed on IDEAS

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    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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