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Probabilistic quantile factor analysis

Dimitris KorobilisMaximilian Schr\"oder
Dec 2022
摘要
This paper extends quantile factor analysis to a probabilistic variant thatincorporates regularization and computationally efficient variationalapproximations. By means of synthetic and real data experiments it isestablished that the proposed estimator can achieve, in many cases, betteraccuracy than a recently proposed loss-based estimator. We contribute to theliterature on measuring uncertainty by extracting new indexes of low, mediumand high economic policy uncertainty, using the probabilistic quantile factormethodology. Medium and high indexes have clear contractionary effects, whilethe low index is benign for the economy, showing that not all manifestations ofuncertainty are the same.
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