Local and global spatial effects in hierarchical models

Donald J. Lacombe, Stuart G. McIntyre

Research output: Contribution to journalArticle

4 Citations (Scopus)
139 Downloads (Pure)

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.
Original languageEnglish
Number of pages6
JournalApplied Economics Letters
Early online date10 Feb 2016
DOIs
Publication statusE-pub ahead of print - 10 Feb 2016

Keywords

  • spatial econometrics
  • hierarchical models
  • Bayesian

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  • Research Output

    • 4 Citations
    • 1 Paper

    Bayesian estimation of the multilevel/hierarchical spatially autocorrelated random intercept model

    McIntyre, S. & Lacombe, D., 28 Mar 2014, (Unpublished).

    Research output: Contribution to conferencePaper

  • Activities

    • 1 Membership of committee

    Regional Research Institute (External organisation)

    Stuart McIntyre (Member)

    1 Jun 2013 → …

    Activity: Membership typesMembership of committee

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