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.

中文翻译：

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因子模型的最新发展及其在计量经济学学习中的应用

本文选择性地概述了因子模型的最新发展及其在计量经济学学习中的应用。我们关注因子模型的低秩结构的视角，特别提请注意从低秩恢复的角度来估计模型。我们的调查主要包括三个部分。第一部分回顾了基于现代技术的新因子估计，用于恢复高维模型的低秩结构。第二部分讨论了几种因子增强模型的统计推断及其在统计学习模型中的应用。最后一部分从矩阵完成的角度总结了处理不平衡面板的新发展。