IDEAS home Printed from https://ideas.repec.org/a/cup/etheor/v37y2021i4p708-746_3.html
   My bibliography  Save this article

Large Sample Properties Of Bayesian Estimation Of Spatial Econometric Models

Author

Listed:
  • Han, Xiaoyi
  • Lee, Lung-Fei
  • Xu, Xingbai

Abstract

This paper studies asymptotic properties of a posterior probability density and Bayesian estimators of spatial econometric models in the classical statistical framework. We focus on the high-order spatial autoregressive model with spatial autoregressive disturbance terms, due to a computational advantage of Bayesian estimation. We also study the asymptotic properties of Bayesian estimation of the spatial autoregressive Tobit model, as an example of nonlinear spatial models. Simulation studies show that even when the sample size is small or moderate, the posterior distribution of parameters is well approximated by a normal distribution, and Bayesian estimators have satisfactory performance, as classical large sample theory predicts.

Suggested Citation

  • Han, Xiaoyi & Lee, Lung-Fei & Xu, Xingbai, 2021. "Large Sample Properties Of Bayesian Estimation Of Spatial Econometric Models," Econometric Theory, Cambridge University Press, vol. 37(4), pages 708-746, August.
    • Handle: RePEc:cup:etheor:v:37:y:2021:i:4:p:708-746_3
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S0266466620000286/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Doğan, Osman, 2023. "Modified harmonic mean method for spatial autoregressive models," Economics Letters, Elsevier, vol. 223(C).
    2. Abhimanyu Gupta & Xi Qu, 2021. "Consistent specification testing under spatial dependence," Papers 2101.10255, arXiv.org, revised Aug 2022.

    More about this item

    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:cup:etheor:v:37:y:2021:i:4:p:708-746_3. 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: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/ect .

    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.