Bayesian Estimation of the Spatial Durbin Error Model with an Application to Voter Turnout in the 2004 Presidential Election

Donald J. Lacombe, Garth J. Holloway, Timothy M. Shaughnessy

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

The potential for spatial dependence in models of voter turnout, although plausible from a theoretical perspective, has not been adequately addressed in the literature. Using recent advances in Bayesian computation, the authors formulate and estimate the previously unutilized spatial Durbin error model and apply this model to the question of whether spillovers and unobserved spatial dependence in voter turnout matters from an empirical perspective. Formal Bayesian model comparison techniques are employed to compare the normal linear model, the spatially lagged X model (SLX), the spatial Durbin model, and the spatial Durbin error model. The results overwhelmingly support the spatial Durbin error model as the appropriate empirical model.

Original languageEnglish
Pages (from-to)298-327
Number of pages30
JournalInternational Regional Science Review
Volume37
Issue number3
DOIs
StatePublished - Jul 1 2014

Keywords

  • Bayesian inference
  • spatial Durbin error model
  • spatial econometrics

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