Probabilistic Quantile Factor Analysis
提出一种概率分位数因子分析方法，结合正则化和变分近似，在合成和真实数据中比传统损失估计更准确，并提取了低、中、高经济政策不确定性和松、中、紧金融条件的新指数，这些指数对经济活动有预测力。
This article 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 <i>low</i>, <i>medium</i>, and <i>high</i> economic policy uncertainty, as well as <i>loose</i>, <i>median</i>, and <i>tight</i> 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.