Perspectives on spatial econometrics: Linear smoothing with structured models

Daniel P. Mcmillen

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)192-209
Number of pages18
JournalJournal of Regional Science
Volume52
Issue number2
DOIs
StatePublished - May 2012

ASJC Scopus subject areas

  • Development
  • Environmental Science (miscellaneous)

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