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

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  • 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
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    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. Zhu, Yanli & Yin, Li & Lu, Xueyan, 2024. "Peer effects with multifaceted network dependence structures in R&D investment decisions: Evidence from Chinese listed firms," Research in International Business and Finance, Elsevier, vol. 70(PA).
    4. 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.
    5. Kassoum Ayouba, 2023. "Spatial dependence in production frontier models," Journal of Productivity Analysis, Springer, vol. 60(1), pages 21-36, August.
    6. 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.
    7. 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.
    8. 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.

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