Abstract
This article provides a selective overview of the recent developments in factor models and their applications in econometric learning. We focus on the perspective of the low-rank structure of factor models and particularly draw attention to estimating the model from the low-rank recovery point of view. Our survey mainly consists of three parts. The first part is a review of new factor estimations based on modern techniques for recovering low-rank structures of high-dimensional models. The second part discusses statistical inferences of several factor-augmented models and their applications in statistical learning models. The final part summarizes new developments dealing with unbalanced panels from the matrix completion perspective.
Original language | English (US) |
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Pages (from-to) | 401-430 |
Number of pages | 30 |
Journal | Annual Review of Financial Economics |
Volume | 13 |
DOIs | |
State | Published - Nov 1 2021 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Finance
- Economics and Econometrics
Keywords
- Factor adjustments
- Factor models
- High-dimensional statistics
- Matrix completion
- Model selection
- Multiple testing
- Robustness
- Spiked low-rank matrix
- Unbalanced panel