Variable selection for quantile autoregressive model: Bayesian methods versus classical methods

Abstract

In this article, we introduce three Bayesian variable selection methods for the quantile autoregressive model with explanatory variables. The Gibbs sampling algorithms are developed for each method by setting different priors. The numerical simulations suggest that the Gibbs sampling algorithms converge fast and Bayesian variable selection methods are reliable. A real example is given to analysis the relationship between the count of total rental bikes and five explanatory variables. Both simulations and data example indicate that the proposed methods are feasible, reliable, and appropriate for analyzing the Bike Sharing data set.

Keywords:

• Bayesian quantile autoregression
• Bayesian variable selection
• Bayesian order shrinkage
• spike-and-slab prior
• explanatory variables

Acknowledgments

We gratefully acknowledge the anonymous reviewers for their careful work and thoughtful suggestions that have helped improve this article substantially.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is supported by National Natural Science Foundation of China [grant number 11901053], Natural Science Foundation of Jilin Province [grant numbers 20220101038JC, 20210101149JC], Scientific Research Project of Jilin Provincial Department of Education [grant numbers JJKH20220671KJ, JJKH20230665KJ].