IDEAS home Printed from https://ideas.repec.org/a/taf/jnlbes/v40y2022i2p547-558.html
   My bibliography  Save this article

Bayesian Model Averaging for Spatial Autoregressive Models Based on Convex Combinations of Different Types of Connectivity Matrices

Author

Listed:
  • Nicolas Debarsy
  • James P. LeSage

Abstract

There is a great deal of literature regarding use of nongeographically based connectivity matrices or combinations of geographic and non-geographic structures in spatial econometric models. We focus on convex combinations of weight matrices that result in a single weight matrix reflecting multiple types of connectivity, where coefficients from the convex combination can be used for inference regarding the relative importance of each type of connectivity in the global cross-sectional dependence scheme. We tackle the question of model uncertainty regarding selection of the best convex combination by Bayesian model averaging. We use Metropolis–Hastings guided Monte Carlo integration during MCMC estimation of the models to produce log-marginal likelihoods and associated posterior model probabilities. We focus on MCMC estimation, computation of posterior model probabilities, model averaged estimates of the parameters, scalar summary measures of the non-linear partial derivative impacts, and their associated empirical measures of dispersion.

Suggested Citation

  • Nicolas Debarsy & James P. LeSage, 2022. "Bayesian Model Averaging for Spatial Autoregressive Models Based on Convex Combinations of Different Types of Connectivity Matrices," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(2), pages 547-558, April.
  • Handle: RePEc:taf:jnlbes:v:40:y:2022:i:2:p:547-558
    DOI: 10.1080/07350015.2020.1840993
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/07350015.2020.1840993
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/07350015.2020.1840993?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 look for a different version below or search for a different version of it.

    Other versions of this item:

    Citations

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


    Cited by:

    1. Cai, Zhengzheng & Zhu, Yanli & Han, Xiaoyi, 2022. "Bayesian analysis of spatial dynamic panel data model with convex combinations of different spatial weight matrices: A reparameterized approach," Economics Letters, Elsevier, vol. 217(C).
    2. Michele Costola & Matteo Iacopini & Casper Wichers, 2023. "Bayesian SAR model with stochastic volatility and multiple time-varying weights," Papers 2310.17473, arXiv.org.
    3. Gupta, Abhimanyu & Kokas, Sotirios & Michaelides, Alexander & Minetti, Raoul, 2023. "Networks and Information in Credit Markets," Working Papers 2023-1, Michigan State University, Department of Economics.
    4. Kassoum Ayouba, 2023. "Spatial dependence in production frontier models," Journal of Productivity Analysis, Springer, vol. 60(1), pages 21-36, August.
    5. Christian Glocker & Matteo Iacopini & Tam'as Krisztin & Philipp Piribauer, 2023. "A Bayesian Markov-switching SAR model for time-varying cross-price spillovers," Papers 2310.19557, arXiv.org.
    6. Costola, Michele & Iacopini, Matteo & Wichers, Casper, 2023. "Bayesian SAR model with stochastic volatility and multiple time-varying weights," SAFE Working Paper Series 407, Leibniz Institute for Financial Research SAFE.
    7. Christos Agiakloglou & Apostolos Tsimpanos, 2023. "Evaluating the performance of AIC and BIC for selecting spatial econometric models," Journal of Spatial Econometrics, Springer, vol. 4(1), pages 1-35, December.

    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:taf:jnlbes:v:40:y:2022:i:2:p:547-558. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UBES20 .

    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.