Probabilistic Quantile Factor Analysis

TL;DR

This paper introduces a probabilistic extension of quantile factor analysis that uses regularization and variational methods, demonstrating improved accuracy and new economic and financial uncertainty indexes with predictive power.

Contribution

It develops a novel probabilistic quantile factor analysis method with regularization and efficient variational approximations, and introduces new economic and financial uncertainty indexes.

Findings

Proposed estimator outperforms recent loss-based methods in accuracy.

New indexes of economic policy uncertainty and financial conditions are predictive.

Quantile factors outperform mean and median factors in high-dimensional financial data.

Abstract

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.