Bayesian joint quantile autoregression
Abstract
Quantile regression continues to increase in usage, providing a useful alternative to customary mean regression. Primary implementation takes the form of so-called multiple quantile regression, creating a separate regression for each quantile of interest. However, recently, advances have been made in joint quantile regression, supplying a quantile function which avoids crossing of the regression across quantiles. Here, we turn to quantile autoregression (QAR), offering a fully Bayesian version. We extend the initial quantile regression work of Koenker and Xiao (2006) in the spirit of Tokdar and Kadane (2012). We offer a directly interpretable parametric model specification for QAR. Further, we offer a p-th order QAR(p) version, a multivariate QAR(1) version, and a spatial QAR(1) version. We illustrate with simulation as well as a temperature dataset collected in Arag\'on, Spain.
Keywords
Cite
@article{arxiv.2305.19080,
title = {Bayesian joint quantile autoregression},
author = {Jorge Castillo-Mateo and Alan E. Gelfand and Jesús Asín and Ana C. Cebrián and Jesús Abaurrea},
journal= {arXiv preprint arXiv:2305.19080},
year = {2024}
}
Comments
21 pages (+18 pages supplement), 8 figures (+15 figures supplement), 1 table (+6 tables supplement)