PERSPECTIVES ON SPATIAL ECONOMETRICS: LINEAR SMOOTHING WITH STRUCTURED MODELS
説明
<jats:p><jats:bold>ABSTRACT</jats:bold> Though standard spatial econometric models may be useful for specification testing, they rely heavily on a parametric structure that is highly sensitive to model misspecification. The commonly used spatial AR model is a form of spatial smoothing with a structure that closely resembles a semiparametric model. Nonparametric and semiparametric models are generally a preferable approach for more descriptive spatial analysis. Estimated population density functions illustrate the differences between the spatial AR model and nonparametric approaches to data smoothing. A series of Monte Carlo experiments demonstrates that nonparametric predicted values and marginal effect estimates are much more accurate then spatial AR models when the contiguity matrix is misspecified.</jats:p>
収録刊行物
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- Journal of Regional Science
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Journal of Regional Science 52 (2), 192-209, 2012-01-03
Wiley
