Hierarchical spatial econometric models in regional science

Lacombe, Donald J. and McIntyre, Stuart G.; Jackson, Randall and Schaeffer, Peter, eds. (2017) Hierarchical spatial econometric models in regional science. In: Regional Research Frontiers. Advances in Spatial Science: The Regional Science Series, 2 . Springer International Publishing AG, Cham, pp. 151-167. ISBN 9783319505893 (https://doi.org/10.1007/978-3-319-50590-9_9)

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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.

ORCID iDs

Lacombe, Donald J. and McIntyre, Stuart G. ORCID logoORCID: https://orcid.org/0000-0002-0640-7544; Jackson, Randall and Schaeffer, Peter
CORE (COnnecting REpositories)