Local and global spatial effects in hierarchical models

Donald J. Lacombe, Stuart G. McIntyre

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Hierarchical models have a long history in empirical applications; recognition of the fact that many datasets of interest to applied econometricians are nested; counties within states, pupils within school, regions within countries, etc. Just as many datasets are characterized by nesting, many are also characterized by the presence of spatial dependence or spatial heterogeneity. Significant advances have been made in developing econometric techniques and models to allow applied econometricians to address this spatial dimension to their data. This article fuses these two literatures together and combines a hierarchical model with the two general spatial econometric models.

Original languageEnglish
Pages (from-to)1168-1172
Number of pages5
JournalApplied Economics Letters
Volume23
Issue number16
DOIs
StatePublished - Nov 1 2016

Keywords

  • Bayesian
  • Spatial econometrics
  • hierarchical
  • multilevel

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