Hierarchical Spatial Econometric Models in Regional Science

Donald J. Lacombe () and Stuart McIntyre
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Donald J. Lacombe: Texas Tech University

Chapter Chapter 9 in Regional Research Frontiers - Vol. 2, 2017, pp 151-167 from Springer

Abstract: 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 (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/978-3-319-50590-9_9

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