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摘要翻译：
本文对因子模型的最新发展及其在统计学习中的应用作了有选择的综述。本文着重研究因子模型的低阶结构，特别是从低阶恢复的角度对模型进行估计。本文主要由三个部分组成：第一部分是对基于现代技术的高维模型低阶结构恢复的新因子估计方法的综述。第二部分讨论了几种因子增广模型的统计推论及其在计量经济学习模型中的应用。最后总结了从矩阵完成的角度处理非平衡面板的新进展。
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英文标题：
《Recent Developments on Factor Models and its Applications in Econometric
Learning》
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作者：
Jianqing Fan, Kunpeng Li, Yuan Liao
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最新提交年份：
2020
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
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.
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PDF链接：
https://arxiv.org/pdf/2009.10103
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