Bayesian joint quantile autoregression
| Autor: | Castillo-Mateo, Jorge, Gelfand, Alan E., Asín, Jesús, Cebrián, Ana C., Abaurrea, Jesús |
|---|---|
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | TEST 33(1), 335-357 (2024) |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1007/s11749-023-00895-6 |
| Popis: | 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. Comment: 21 pages (+18 pages supplement), 8 figures (+15 figures supplement), 1 table (+6 tables supplement) |
| Databáze: | arXiv |
| Externí odkaz: |
|