A Bayesian Variable Selection Method for Spatial Autoregressive Quantile Models

Yuanying Zhao () and Dengke Xu
Additional contact information
Yuanying Zhao: College of Mathematics and Information Science, Guiyang University, Guiyang 550005, China
Dengke Xu: School of Economics, Hangzhou Dianzi University, Hangzhou 310018, China

Mathematics, 2023, vol. 11, issue 4, 1-19

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.

Keywords: Bayesian variable selection; quantile regression; spatial autoregressive models; Gibbs sampling; PIT algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/4/987/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/4/987/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:4:p:987-:d:1069061

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().