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Recent Developments in Factor Models and Applications in Econometric Learning
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DOI：10.1146/annurev-financial-091420-011735.png)
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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.
Keyword:
factor models
spiked low-rank matrix
matrix completion
unbalanced panel
factor adjustments
robustness
model selection
multiple testing
high-dimensional statistics
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