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
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.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chen, L., Dolado, J. J., and Gonzalo, J (2021) Quantile factor models | 1.000 | 41 | 5 | 100% |
| 2 | Blei, D. M., Kucukelbir, A., and McAuliffe, J. D (2017) Variational inference: A review for statisticians | 0.928 | 4 | 3 | 100% |
| 3 | Stock, J. H. and Watson, M. W (2002) Forecasting using principal components from a large number of predictors | 0.928 | 4 | 3 | 100% |
| 4 | Tipping, M. E (2001) Sparse bayesian learning and the relevance vector machine | 0.928 | 4 | 3 | 100% |
| 5 | Andreou, E., Ghysels, E., and Kourtellos, A (2013) Should macroeconomic forecasters use daily financial data and how? | 0.874 | 6 | 2 | 100% |
| 6 | Lim, D., Park, B., Nott, D., Wang, X., and Choi, T (2020) Sparse signal shrinkage and outlier detection in high-dimensional quantile regression with variational bayes | 0.843 | 3 | 3 | 100% |
| 7 | Baker, S. R., Bloom, N., and Davis, S. J (2016) Measuring economic policy uncertainty | 0.644 | 2 | 2 | 100% |
| 8 | Ghahramani, Z. and Beal, M. J (1999) Variational inference for bayesian mixtures of factor analysers | 0.644 | 2 | 2 | 100% |
| 9 | Bhattacharya, A. and Dunson, D. B (2011) Sparse Bayesian infinite factor models | 0.644 | 2 | 2 | 100% |
| 10 | Koenker, R (2005) Quantile Regression | 0.644 | 2 | 2 | 100% |
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