Horseshoe prior Bayesian quantile regression

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Authors

Date

2024-01

Department

Department of Computer Science
Heriot-Watt University
Department of Computer Science

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Mcode

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Language

en

Pages

28
193-220

Series

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C: APPLIED STATISTICS, Volume 73, issue 1

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.

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Keywords

Monte Carlo, Global-local prior, Growth-at-risk, Quantile regression, Sampling method

Other note

DOI

10.1093/jrsssc/qlad091

Citation

Kohns , D & Szendrei , T 2024 , ' Horseshoe prior Bayesian quantile regression ' , JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C: APPLIED STATISTICS , vol. 73 , no. 1 , qlad091 , pp. 193-220 . https://doi.org/10.1093/jrsssc/qlad091

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https://urn.fi/URN:NBN:fi:aalto-202402072377

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[article-cris] Perustieteiden korkeakoulu / SCI