Liang Chen, Juan Jose Dolado, Jesus Gonzalo
arXiv 6 Nov 2019 · Econometrics · 1 citations (OpenAlex)
arXiv:1911.02173 · PDF · DOI · OpenAlex · Extracted main text
Quantile Factor Models (QFM) represent a new class of factor models for high-dimensional panel data. Unlike Approximate Factor Models (AFM), where only location-shifting factors can be extracted, QFM also allow to recover unobserved factors shifting other relevant parts of the distributions of observed variables. A quantile regression approach, labeled Quantile Factor Analysis (QFA), is proposed to consistently estimate all the quantile-dependent factors and loadings. Their asymptotic distribution is then derived using a kernel-smoothed version of the QFA estimators. Two consistent model selection criteria, based on information criteria and rank minimization, are developed to determine the number of factors at each quantile. Moreover, in contrast to the conditions required for the use of Principal Components Analysis in AFM, QFA estimation remains valid even when the idiosyncratic errors have heavy-tailed distributions. Three empirical applications (regarding macroeconomic, climate and finance panel data) provide evidence that extra factors shifting the quantiles other than the means could be relevant in practice.
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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 | Bai, J (2003) Inferential theory for factor models of large dimensions | 1.000 | 13 | 3 | 100% |
| 2 | Chen, M., I. Fernández-Val, and M. Weidner (2018) Nonlinear factor models for network and panel data | 0.811 | 4 | 2 | 100% |
| 3 | Adrian, T., N. Boyarchenko, and D. Giannone (2019) Vulnerable growth | 0.737 | 3 | 2 | 100% |
| 4 | Galvao, A. F. and K. Kato (2016) Smoothed quantile regression for panel data | 0.737 | 3 | 2 | 100% |
| 5 | Stock, J. H. and M. W. Watson (2002) Forecasting using principal components from a large number of predictors | 0.737 | 3 | 2 | 100% |
| 6 | Renault, E., T. Van Der Heijden, and B. Werker (2017) Arbitrage pricing theory for idiosyncratic variance factors | 0.644 | 2 | 2 | 100% |
| 7 | Stock, J. H. and M. W. Watson (2011) Dynamic factor models | 0.644 | 2 | 2 | 100% |
| 8 | van der Vaart, A. and J. Wellner (1996) Weak convergence and empirical processes | 0.644 | 2 | 2 | 100% |
| 9 | Chen, L., J. J. Dolado, and J. Gonzalo (2017) Quantile factor models self | 0.585 | 3 | 1 | 100% |
| 10 | Ahn, S. C. and A. R. Horenstein (2013) Eigenvalue ratio test for the number of factors | 0.511 | 2 | 2 | 50% |
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