2020/06/30 by David Kohns, Tibor Szendrei · 1 citation
Decision Sciences · Economics, Econometrics and Finance · Mathematics · #Grey System Theory Applications #Monetary Policy and Economic Impact #Statistical Methods and Inference
paper · pdf · doi:10.1093/jrsssc/qlad091
crossref issued 2023/11/02 · crossref published 2023/11/02 · crossref published-online 2023/11/02 · openalex publication_date 2023/11/02 · crossref created 2023/11/07 · crossref published-print 2024/01/11 · crossref deposited 2024/01/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28 · crossref indexed 2026/07/31
Abstract This paper extends the horseshoe prior to Bayesian quantile regression and provides a fast sampling algorithm for computation in high dimensions. Compared to alternative shrinkage priors, our method yields better performance in coefficient bias and forecast error, especially in sparse designs and in estimating extreme quantiles. In a high-dimensional growth-at-risk forecasting application, we forecast tail risks and complete forecast densities using a database covering over 200 macroeconomic variables. Quantile specific and density calibration score functions show that our method provides competitive performance compared to competing Bayesian quantile regression priors, especially at short- and medium-run horizons.