IDEAS home Printed from https://ideas.repec.org/a/spr/lsprsc/v16y2023i1d10.1007_s12076-023-00362-8.html
   My bibliography  Save this article

Bayesian spatial panel models: a flexible Kronecker error component approach

Author

Listed:
  • Yuheng Ling

    (HaiNan Normal University
    Université Bourgogne-Franche-Comté)

  • Julie Gallo

    (HaiNan Normal University
    Université Bourgogne-Franche-Comté)

Abstract

We introduce a class of spatial panel data models with correlated error components that can simultaneously handle cross-sectional and temporal correlation. These models are based on Gaussian Markov Random Fields with a Kronecker product of separable error covariance matrices, which allows capturing correlations both in time and space while reducing the number of parameters being estimated. We then propose a unified approach for estimating these models using a novel Bayesian approach, known as integrated nested Laplace approximations. An empirical illustration using U.S. cigarette consumption data is given, and we find that the most general model outperforms its competitors in both in-sample fit and forecast performance.

Suggested Citation

  • Yuheng Ling & Julie Gallo, 2023. "Bayesian spatial panel models: a flexible Kronecker error component approach," Letters in Spatial and Resource Sciences, Springer, vol. 16(1), pages 1-11, December.
  • Handle: RePEc:spr:lsprsc:v:16:y:2023:i:1:d:10.1007_s12076-023-00362-8
    DOI: 10.1007/s12076-023-00362-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12076-023-00362-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s12076-023-00362-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Keywords

    Panel data; Spatial error component models; Kronecker product; Bayesian inference; INLA; Gaussian Markov random fields;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    Statistics

    Access and download statistics

    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:lsprsc:v:16:y:2023:i:1:d:10.1007_s12076-023-00362-8. 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.