论文标题
马蹄前贝叶斯分数回归
Horseshoe Prior Bayesian Quantile Regression
论文作者
论文摘要
本文扩展了Carvalho等人的马蹄。 （2010）至贝叶斯分位回归（HS-BQR），并为高维度计算提供了快速采样算法。在蒙特卡洛模拟和美国对美国的高维生长风险（GAR）预测应用中评估了所提出的HS-BQR的性能。与替代性收缩先验相比，所提出的HS-BQR在系数偏差和预测误差中的表现更好（或最差相似）。 HS-BQR在稀疏设计和估计极端分位数方面尤其有效。正如预期的那样，模拟还强调，在密集的DGP中识别各个回归器的分位数特定位置和比例效应需要大量数据。在GAR应用程序中，我们使用McCracken和NG（2020）数据库预测尾巴风险以及完整的预测密度。分位数特异性和密度校准评分函数表明，HS-BQR提供了最佳性能，尤其是在短和中型跑步范围内。在大数据上下文中产生良好校准的密度预测和准确的下行风险度量的能力使HS-BQR成为现实应用程序和衰退建模的有前途的工具。
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 worst similar) performance in coefficient bias and forecast error. The HS-BQR is particularly potent in sparse designs and in estimating extreme quantiles. As expected, the simulations also highlight that identifying quantile specific location and scale effects for individual regressors in dense DGPs requires substantial data. In the GaR application, we forecast tail risks as well as complete forecast densities using the McCracken and Ng (2020) database. Quantile specific and density calibration score functions show that the HS-BQR provides the best performance, especially at short and medium run horizons. The ability to produce well calibrated density forecasts and accurate downside risk measures in large data contexts makes the HS-BQR a promising tool for nowcasting applications and recession modelling.
