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A Bayesian Variable Selection Method for Spatial Autoregressive Quantile Models

Author

Listed:
  • Yuanying Zhao

    (College of Mathematics and Information Science, Guiyang University, Guiyang 550005, China)

  • Dengke Xu

    (School of Economics, Hangzhou Dianzi University, Hangzhou 310018, China)

Abstract

In this paper, a Bayesian variable selection method for spatial autoregressive (SAR) quantile models is proposed on the basis of spike and slab prior for regression parameters. The SAR quantile models, which are more generalized than SAR models and quantile regression models, are specified by adopting the asymmetric Laplace distribution for the error term in the classical SAR models. The proposed approach could perform simultaneously robust parametric estimation and variable selection in the context of SAR quantile models. Bayesian statistical inferences are implemented by a detailed Markov chain Monte Carlo (MCMC) procedure that combines Gibbs samplers with a probability integral transformation (PIT) algorithm. In the end, empirical numerical examples including several simulation studies and a Boston housing price data analysis are employed to demonstrate the newly developed methodologies.

Suggested Citation

  • Yuanying Zhao & Dengke Xu, 2023. "A Bayesian Variable Selection Method for Spatial Autoregressive Quantile Models," Mathematics, MDPI, vol. 11(4), pages 1-19, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:987-:d:1069061
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    References listed on IDEAS

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