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Mostly Pointless Spatial Econometrics?

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  • Steve Gibbons
  • Henry G. Overman

Abstract

We argue that identification problems bedevil most applied spatial research. Spatial econometrics solves these problems by deriving estimators assuming that functional forms are known and by using model comparison techniques to let the data choose between competing specifications. We argue that in most situations of interest this, at best, achieves only very weak identification. Worse, in most cases, such an approach will simply be uninformative about the economic processes at work rendering much applied spatial econometric research 'pointless', unless the main aim is simply description of the data. We advocate an alternative approach based on the 'experimental paradigm' which puts issues of identification and causality at centre stage.

Suggested Citation

  • Steve Gibbons & Henry G. Overman, 2010. "Mostly Pointless Spatial Econometrics?," SERC Discussion Papers 0061, Spatial Economics Research Centre, LSE.
  • Handle: RePEc:cep:sercdp:0061
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    Keywords

    statistical methods; spatial; modeling;

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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