Using Bayesian posterior model probabilities to identify omitted variables in spatial regression models

Donald J. Lacombe, James P. Lesage

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

LeSage and Pace (2009) consider the impact of omitted variables in the face of spatial dependence in the disturbance process of a linear regression relationship and show that this can lead to a spatial Durbin model. Monte Carlo experiments and Bayesian model comparison methods are used to distinguish between spatial error and Durbin model specifications that arise with varying levels of correlation between included and omitted variables. The Monte Carlo results suggest use of the common factor relationship developed in Burridge (1981) as a way to test for the presence of omitted variables bias influencing specific explanatory variables.

Original languageEnglish
Pages (from-to)365-383
Number of pages19
JournalPapers in Regional Science
Volume94
Issue number2
DOIs
StatePublished - Jun 1 2015

Keywords

  • Bayesian model comparison methods
  • Common factor relationship
  • Global spatial spillovers

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