Recent Developments on Factor Models and its Applications in Econometric Learning
This is an incremental survey paper summarizing existing methods for researchers in econometrics and statistical learning.
This paper surveys recent developments in factor models, focusing on low-rank structure recovery techniques for high-dimensional data and their applications in econometric learning, including factor-augmented models and unbalanced panel data handling.
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.