Probabilistic Quantile Factor Analysis

Dimitris Korobilis and Maximilian Schr\"oder

Papers from arXiv.org

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.

Date: 2022-12, Revised 2024-08
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (11)

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http://arxiv.org/pdf/2212.10301 Latest version (application/pdf)

Related works:
Working Paper: Probabilistic Quantile Factor Analysis (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2212.10301

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