Probabilistic Quantile Factor Analysis

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.