Recent Developments on Factor Models and its Applications in Econometric Learning

TL;DR

This survey reviews recent advances in factor models emphasizing low-rank structures, estimation techniques, statistical inference, and applications in econometric learning, including handling unbalanced panels through matrix completion.

Contribution

It provides a comprehensive overview of modern low-rank recovery methods and their applications in econometric learning, highlighting recent developments and techniques.

Findings

New factor estimation methods based on low-rank recovery.

Enhanced statistical inference for factor-augmented models.

Innovative approaches to unbalanced panel data using matrix completion.

Abstract

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.