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Hierarchical Spatial Econometric Models in Regional Science

  • Donald J. LacombeEmail author
  • Stuart G. McIntyre
Chapter
Part of the Advances in Spatial Science book series (ADVSPATIAL)

Abstract

Hierarchical econometric models have a long history in applied research. Recent advances have seen the development of spatial hierarchical econometric models, fusing the advantages of hierarchical modeling with those of spatial econometrics. Many datasets used to investigate key questions in regional science are inherently nested: individuals within counties, counties within states, regions within countries, etc. Being able to reflect this nesting within the econometric framework will be essential to future applied work in regional science. This chapter begins by introducing the key elements of spatial and non-spatial hierarchical econometric models before briefly reviewing existing econometric work using these models. Thereafter, we focus on different types of future development of these models and their uses in regional science.

Keywords

Marginal Likelihood Spatial Weight Matrix Spatial Error Model Random Coefficient Model Posterior Model Probability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Personal Financial PlanningTexas Tech UniversityLubbockUSA
  2. 2.Department of EconomicsUniversity of StrathclydeGlasgowUK

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