IDEAS home Printed from https://ideas.repec.org/h/spr/adspcp/978-3-319-50590-9_9.html
   My bibliography  Save this book chapter

Hierarchical Spatial Econometric Models in Regional Science

In: Regional Research Frontiers - Vol. 2

Author

Listed:
  • Donald J. Lacombe

    (Texas Tech University)

  • Stuart G. McIntyre

    (University of Strathclyde)

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.

Suggested Citation

  • Donald J. Lacombe & Stuart G. McIntyre, 2017. "Hierarchical Spatial Econometric Models in Regional Science," Advances in Spatial Science, in: Randall Jackson & Peter Schaeffer (ed.), Regional Research Frontiers - Vol. 2, chapter 0, pages 151-167, Springer.
    • Handle: RePEc:spr:adspcp:978-3-319-50590-9_9
    DOI: 10.1007/978-3-319-50590-9_9
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jing Chen, 2017. "Geographical Scale, Industrial Diversity and Regional Economic Stability," Working Papers Working Paper 2017-03, Regional Research Institute, West Virginia University.
    2. Joshua C. Hall & Donald J. Lacombe & Amir Neto & James Young, 2022. "Bayesian Estimation of the Hierarchical SLX Model with an Application to Housing Markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 46(2), pages 360-373, April.
    3. Alberto Díaz Dapena & Fernando Rubiera-Morollon & Dusan Paredes, 2019. "New Approach to Economic Convergence in the EU: A Multilevel Analysis from the Spatial Effects Perspective," International Regional Science Review, , vol. 42(3-4), pages 335-367, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:adspcp:978-3-319-50590-9_9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.