Hierarchical spatial econometric models in regional science

Donald J. Lacombe, Stuart G. McIntyre

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationRegional Research Frontiers
Subtitle of host publicationMethodological Advances, Regional Systems Modeling and Open Sciences
EditorsRandall Jackson, Peter Schaeffer
Place of PublicationCham
PublisherSpringer International Publishing AG
Pages151-167
Number of pages17
Volume2
ISBN (Print)9783319505893
DOIs
Publication statusPublished - 18 Apr 2017

Publication series

NameAdvances in Spatial Science: The Regional Science Series
PublisherSpringer International Publishing
ISSN (Print)1430-9602

Keywords

  • hierarchical models
  • regional science
  • econometric models
  • administrative hierarchy
  • local government
  • local authorities
  • spatial
  • heteroskedasticity
  • origin-destination

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