论文标题
概率分位数分析
Probabilistic Quantile Factor Analysis
论文作者
论文摘要
本文将分位数因子分析扩展到概率变体，该变体融合了正则化和计算有效的变分近似值。我们通过合成和真实的数据实验建立，在许多情况下，提出的估计器可以比最近提出的基于损失的估计器获得更好的准确性。我们通过提取\ emph {low}，\ emph {edimph {中}和\ emph {high}经济政策不确定性以及\ emph {lose}，\ emph {中间}和\ emph {permph {permph {紧密}财务条件来为因素分析文献做出贡献。我们表明，高不确定性和严格的财务状况指数具有各种经济活动措施的卓越预测能力。在涉及大约1000次每日财务系列的高维活动中，我们发现与平均因素或中位数相比，分数因素还提供了较高的样本外信息。
This paper extends quantile factor analysis to a probabilistic variant that incorporates regularization and computationally efficient variational approximations. We establish through synthetic and real data experiments that the proposed estimator can, in many cases, achieve better accuracy than a recently proposed loss-based estimator. We contribute to the factor analysis literature by extracting new indexes of \emph{low}, \emph{medium}, and \emph{high} economic policy uncertainty, as well as \emph{loose}, \emph{median}, and \emph{tight} financial conditions. We show that the high uncertainty and tight financial conditions indexes have superior predictive ability for various measures of economic activity. In a high-dimensional exercise involving about 1000 daily financial series, we find that quantile factors also provide superior out-of-sample information compared to mean or median factors.
