摘要翻译：
本文推广了Carvalho等人的马蹄先验。（2010）为贝叶斯分位数回归（HS-BQR）提供了一种用于高维计算的快速采样算法。在Monte Carlo模拟和美国的高维GaR预测应用中，对所提出的HS-BQR的性能进行了评估。Monte Carlo设计考虑了几种稀疏性和误差结构。与替代收缩先验相比，所提出的HS-BQR在系数偏差和预测误差方面具有更好的（或最坏情况下相似）性能。HS-BQR在稀疏设计和极值分位数估计方面特别有效。正如预期的那样，模拟还强调指出，在密集DGPs中识别单个回归子的分位数特定位置和尺度效应需要大量数据。在GaR应用中，我们使用McCracken and Ng(2020)数据库预测尾部风险以及完整的预测密度。分位数比和密度校准得分函数表明，HS-BQR提供了最佳性能，尤其是在短和中运行层位。HS-BQR能够在大数据背景下产生精确的密度预测和准确的下行风险度量，这使得它成为临近预报应用和衰退建模的一个有前途的工具。
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英文标题：
《Horseshoe Prior Bayesian Quantile Regression》
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作者：
David Kohns and Tibor Szendrei
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最新提交年份：
2021
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
  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.
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PDF链接：
https://arxiv.org/pdf/2006.07655