Horseshoe prior Bayesian quantile regression

David Kohns*, Tibor Szendrei

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
33 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)193-220
Number of pages28
JournalJournal of the Royal Statistical Society. Series C: Applied Statistics
Volume73
Issue number1
Early online date2 Nov 2023
DOIs
Publication statusPublished - Jan 2024

Keywords

  • global–local prior
  • growth-at-risk
  • Monte Carlo
  • quantile regression
  • sampling method

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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