Recent Developments in Factor Models and Applications in Econometric Learning

被引:6
|
作者
Fan, Jianqing [1 ]
Li, Kunpeng [2 ]
Liao, Yuan [3 ]
机构
[1] Princeton Univ, Dept Operat Res & Financial Engn, Princeton, NJ 08544 USA
[2] Capital Univ Econ & Business, Int Sch Econ & Management, Beijing 100070, Peoples R China
[3] Rutgers State Univ, Dept Econ, New Brunswick, NJ 08901 USA
基金
中国国家自然科学基金;
关键词
factor models; spiked low-rank matrix; matrix completion; unbalanced panel; factor adjustments; robustness; model selection; multiple testing; high-dimensional statistics; MAXIMUM-LIKELIHOOD-ESTIMATION; LEAST-SQUARES ESTIMATOR; DYNAMIC FACTOR MODELS; FALSE DISCOVERY RATE; PRINCIPAL COMPONENTS; MATRIX COMPLETION; CONVEX RELAXATION; CROSS-SECTION; OPTIMAL RATES; INFERENCE;
D O I
10.1146/annurev-financial-091420-011735
中图分类号
F [经济];
学科分类号
02 ;
摘要
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.
引用
收藏
页码:401 / 430
页数:30
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