Local and global spatial effects in hierarchical models

Donald J. Lacombe, Stuart G. McIntyre

Research output: Contribution to journalArticle

3 Citations (Scopus)

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 paper fuses these two literatures together and combines a hierarchical model with the two general spatial econometric models.
LanguageEnglish
Number of pages6
JournalApplied Economics Letters
Early online date10 Feb 2016
DOIs
Publication statusE-pub ahead of print - 10 Feb 2016

Fingerprint

Hierarchical model
Spatial effects
Econometric models
Spatial econometrics
Spatial dependence
Spatial heterogeneity
Econometrics

Keywords

  • spatial econometrics
  • hierarchical models
  • Bayesian

Cite this

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Local and global spatial effects in hierarchical models. / Lacombe, Donald J.; McIntyre, Stuart G.

In: Applied Economics Letters, 10.02.2016.

Research output: Contribution to journalArticle

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