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Probabilistic Quantile Factor Analysis

Dimitris Korobilis, Maximilian Schröder

arXiv 20 Dec 2022 · Econometrics · publishedJournal of Business and Economic Statistics (2024) · 5 citations (OpenAlex)

arXiv:2212.10301 · PDF · DOI · OpenAlex · Extracted main text

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 low, medium, and high economic policy uncertainty, as well as loose, median, and 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.

Citation extraction

39
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in-text mentions
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distinct cited
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Chen, L., Dolado, J. J., and Gonzalo, J (2021) Quantile factor models1.000415100%
2Blei, D. M., Kucukelbir, A., and McAuliffe, J. D (2017) Variational inference: A review for statisticians0.92843100%
3Stock, J. H. and Watson, M. W (2002) Forecasting using principal components from a large number of predictors0.92843100%
4Tipping, M. E (2001) Sparse bayesian learning and the relevance vector machine0.92843100%
5Andreou, E., Ghysels, E., and Kourtellos, A (2013) Should macroeconomic forecasters use daily financial data and how?0.87462100%
6Lim, D., Park, B., Nott, D., Wang, X., and Choi, T (2020) Sparse signal shrinkage and outlier detection in high-dimensional quantile regression with variational bayes0.84333100%
7Baker, S. R., Bloom, N., and Davis, S. J (2016) Measuring economic policy uncertainty0.64422100%
8Ghahramani, Z. and Beal, M. J (1999) Variational inference for bayesian mixtures of factor analysers0.64422100%
9Bhattacharya, A. and Dunson, D. B (2011) Sparse Bayesian infinite factor models0.64422100%
10Koenker, R (2005) Quantile Regression0.64422100%

Showing the top 10 of 39 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Monitoring multicountry macroeconomic risk\@thefnmark\@footnotetextWe would like to thank Raffaella Giacomini, Sylvia Kaufmann, Massimiliano Marcellino, Christian Matthes, Mirco Rubin, Neil Shephard, Leif Anders Thorsrud and participants at the following conferences, for useful discussions and comments: 12th European Seminar on Bayesian Econometrics in Salzburg; “Advances in alternative data and machine learning for macroeconomics and finance” in Paris; Barcelona Workshop on Financial Econometrics; 27th International Conference on Macroeconomic Analysis and International Finance in Rethymno; 2023 Finance and Business Analytics Conference in Lefkada; 10th IAAE Annual Conference in Oslo. We would also like to thank seminar participants at the following institutions: BI Norwegian Business School, European Central Bank, University of Lancaster. The views expressed are those of the authors and do not necessarily reflect those of Norges Bank or any of the affiliated institutions0.909124
2Bayesian Multivariate Quantile Regression with alternative Time-varying Volatility Specifications0.40511
3Nonlinearities in Macroeconomic Tail Risk through the Lens of Big Data Quantile Regressions0.40511
4Agreed and Disagreed Uncertainty0.40511
5A Quantile Nelson-Siegel model0.40511