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Probabilistic Quantile Factor Analysis
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DOI：10.1080/07350015.2024.2396956.png)
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Abstract
En 中文
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 oflow,medium, andhigheconomic policy uncertainty, as well asloose,median, andtightfinancial 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.
Keywords:
Penalized factors
Quantile regression
Variational Bayes
Journal
J
IF:
2.5
Papers:
96
Citations:
9.1K