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Horseshoe Prior Bayesian Quantile Regression
[article]
2021
arXiv
pre-print
This paper extends the horseshoe prior of Carvalho et al. (2010) to Bayesian quantile regression (HS-BQR) and provides a fast sampling algorithm for computation in high dimensions. The performance of the proposed HS-BQR is evaluated on Monte Carlo simulations and a high dimensional Growth-at-Risk (GaR) forecasting application for the U.S. The Monte Carlo design considers several sparsity and error structures. Compared to alternative shrinkage priors, the proposed HS-BQR yields better (or at
arXiv:2006.07655v2
fatcat:o5m64l2aqrebba7u7bnydgizyu