Jianqing Fan, Kunpeng Li, Yuan Liao
arXiv 21 Sep 2020 · Econometrics · publishedAnnual Review of Financial Economics (2021) · 23 citations (OpenAlex)
arXiv:2009.10103 · PDF · DOI · OpenAlex · Extracted main text
This paper makes a selective survey on the recent development of the factor model and its application on statistical learnings. We focus on the perspective of the low-rank structure of factor models, and particularly draws attentions to estimating the model from the low-rank recovery point of view. The survey mainly consists of three parts: the first part is a review on new factor estimations based on modern techniques on recovering low-rank structures of high-dimensional models. The second part discusses statistical inferences of several factor-augmented models and applications in econometric learning models. The final part summarizes new developments dealing with unbalanced panels from the matrix completion perspective.
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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 | Fan, J. and Liao, Y (2020) Learning latent factors from diversified projections and its applications to over-estimated and weak factors self | 0.874 | 5 | 2 | 100% |
| 2 | Fan, J., Li, R., Zhang, C.-H. and Zou, H (2020) Statistical Foundations of Data Science self | 0.811 | 4 | 2 | 100% |
| 3 | Giglio, S., Liao, Y. and Xiu, D (2020) Thousands of alpha tests self | 0.811 | 4 | 2 | 100% |
| 4 | Bai, J. and Li, K (2012) Statistical analysis of factor models of high dimension self | 0.737 | 3 | 2 | 100% |
| 5 | Bai, J. and Ng, S (2009) Boosting diffusion indices | 0.693 | 6 | 1 | 100% |
| 6 | Lee, S., Liao, Y., Seo, M. and Y., S (2020) Factor-driven two-regime regression self | 0.693 | 5 | 1 | 100% |
| 7 | Su, L., Miao, K. and Jin, S (2019) On factor models with random missing: Em estimation, inference, and cross validation | 0.693 | 5 | 1 | 100% |
| 8 | Agarwal, A., Negahban, S., Wainwright, M. J. et al (2012) Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions | 0.644 | 4 | 1 | 100% |
| 9 | Bai, J. and Ng, S (2019) Matrix completion, counterfactuals, and factor analysis of missing data | 0.644 | 4 | 1 | 100% |
| 10 | Chen, Y., Fan, J., Ma, C. and Yan, Y (2019) Inference and uncertainty quantification for noisy matrix completion self | 0.644 | 4 | 1 | 100% |
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