• Media type: E-Book
  • Title: Probabilistic Quantile Factor Analysis
  • Contributor: Korobilis, Dimitris [VerfasserIn]; Schröder, Maximilian [VerfasserIn]
  • imprint: [S.l.]: SSRN, [2023]
  • Extent: 1 Online-Ressource (41 p)
  • Language: English
  • DOI: 10.2139/ssrn.4450188
  • Identifier:
  • Keywords: variational Bayes ; penalized factors ; quantile regression
  • Origination:
  • Footnote: Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments December 20, 2022 erstellt
  • Description: This paper extends quantile factor analysis to a probabilistic variant that incorporates regularization and computationally efficient variational approximations. By means of synthetic and real data experiments it is established that the proposed estimator can achieve, in many cases, better accuracy than a recently proposed loss-based estimator. We contribute to the literature on measuring uncertainty by extracting new indexes of \emph{low}, \emph{medium} and \emph{high} economic policy uncertainty, using the probabilistic quantile factor methodology. Medium and high indexes have clear contractionary effects, while the low index is benign for the economy, showing that not all manifestations of uncertainty are the same
  • Access State: Open Access