IDEAS home Printed from https://ideas.repec.org/a/spr/testjl/v33y2024i4d10.1007_s11749-024-00944-8.html
   My bibliography  Save this article

Higher-order spatial autoregressive varying coefficient model: estimation and specification test

Author

Listed:
  • Tizheng Li

    (Xi’an University of Architecture and Technology)

  • Yuping Wang

    (Xi’an University of Architecture and Technology)

Abstract

Conventional higher-order spatial autoregressive models assume that regression coefficients are constant over space, which is overly restrictive and unrealistic in applications. In this paper, we introduce higher-order spatial autoregressive varying coefficient model where regression coefficients are allowed to smoothly change over space, which enables us to simultaneously explore different types of spatial dependence and spatial heterogeneity of regression relationship. We propose a semi-parametric generalized method of moments estimation method for the proposed model and derive asymptotic properties of resulting estimators. Moreover, we propose a testing method to detect spatial heterogeneity of the regression relationship. Simulation studies show that the proposed estimation and testing methods perform quite well in finite samples. The Boston house price data are finally analyzed to demonstrate the proposed model and its estimation and testing methods.

Suggested Citation

  • Tizheng Li & Yuping Wang, 2024. "Higher-order spatial autoregressive varying coefficient model: estimation and specification test," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 33(4), pages 1258-1299, December.
    • Handle: RePEc:spr:testjl:v:33:y:2024:i:4:d:10.1007_s11749-024-00944-8
    DOI: 10.1007/s11749-024-00944-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11749-024-00944-8
    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/s11749-024-00944-8?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.

    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:testjl:v:33:y:2024:i:4:d:10.1007_s11749-024-00944-8. 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.