On model specification and parameter space definitions in higher order spatial econometric models

J. Paul Elhorst, Donald J. Lacombe, Gianfranco Piras

Research output: Contribution to journalArticlepeer-review

46 Scopus citations

Abstract

Higher-order spatial econometric models that include more than one weights matrix have seen increasing use in the spatial econometrics literature. There are two distinct issues related to the specification of these extended models. The first issue is what form the higher-order spatial econometric model takes, i.e. higher-order polynomials in the spatial weights matrices vs. higher-order spatial autoregressive processes. The second issue relates to the parameter space in such models and how this can affect the choice of model specification, estimation, and inference. We outline a procedure that is simple both mathematically and computationally for finding the stationary region for spatial econometric models with up to K weights matrices for higher-order spatial autoregressive processes. We also compare and contrast this approach with the parameter space for models that incorporate higher-order polynomials in the spatial weights matrices. Regardless of the model utilized in empirical practice, ignoring the relevant parameter region can lead to incorrect inferences regarding both the nature of the spatial autocorrelation process and the effects of changes in covariates on the dependent variable.

Original languageEnglish
Pages (from-to)211-220
Number of pages10
JournalRegional Science and Urban Economics
Volume42
Issue number1-2
DOIs
StatePublished - Jan 2012

Keywords

  • Higher order spatial models
  • Parameter space
  • Spatial econometrics

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