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

Stuart McIntyre, Donald Lacombe

Research output: Contribution to conferencePaper

Abstract

Multilevel or Hierarchical models are statistical models that allow for parameter estimation at more than one level. Many empirical research questions in regional science involve hierarchically--structured data, e.g counties nested within states. Standard normal linear models are ill--suited to estimate these models because they ignore by assumption this nesting of the data. One type of hierarchical model is the random intercept models; it allow for the separate estimation of an intercept for each group at the second level of the hierarchy, e.g. states in a model with counties nested within states. To date, little work has been done on capturing spatial dynamics in hierarchical models; for instance the idea that the random intercept at the second (or group) level in the hierarchical model might follow a spatial process. In this paper we introduce a model that considers the hierarchical random intercept term as following a spatial autoregressive process. Intuitively, if we consider the upper level random intercept term in this way, we are able to test whether or not there is spatial dependence in the random intercepts between the upper level groupings (states for instance). Using now familiar Bayesian computational techniques, this paper derives and illustrates the hierarchical spatially autocorrelated random intercept model that includes covariate information at the second level of the hierarchy. This model has a number of attractive potential applications, for instance to examine crime patterns within neighbourhoods nested within police divisions, or looking at individual labour market outcomes, while nesting individuals within interlinked local labour market areas.
Original languageEnglish
Publication statusUnpublished - 28 Mar 2014
Event2014 Annual Meeting of the Southern Regional Science Association - Texas, San Antonio, United States
Duration: 27 Mar 201429 Mar 2014
http://conference.srsa.org/ocs/index.php/SRSA/SRSA2014/

Conference

Conference2014 Annual Meeting of the Southern Regional Science Association
Abbreviated titleSRSA
CountryUnited States
CitySan Antonio
Period27/03/1429/03/14
Internet address

Keywords

  • multilevel/hierarchical model
  • spatial autocorrelated random intercept model

Fingerprint Dive into the research topics of 'Bayesian estimation of the multilevel/hierarchical spatially autocorrelated random intercept model'. Together they form a unique fingerprint.

  • Research Output

    Local and global spatial effects in hierarchical models

    Lacombe, D. J. & McIntyre, S. G., 10 Feb 2016, In : Applied Economics Letters. 6 p.

    Research output: Contribution to journalArticle

    Open Access
    File
  • 4 Citations (Scopus)
    139 Downloads (Pure)

    Cite this

    McIntyre, S., & Lacombe, D. (2014). Bayesian estimation of the multilevel/hierarchical spatially autocorrelated random intercept model. Paper presented at 2014 Annual Meeting of the Southern Regional Science Association, San Antonio, United States.