Sparse High-Dimensional Models in Economics

被引：104

作者：

Fan, Jianqing ^{[1
,2
]}

Lv, Jinchi ^{[3
]}

Qi, Lei ^{[1
,2
]}

机构：

[1] Princeton Univ, Bendheim Ctr Finance, Princeton, NJ 08544 USA

[2] Princeton Univ, Dept Operat Res & Financial Engn, Princeton, NJ 08544 USA

[3] Univ So Calif, Marshall Sch Business, Informat & Operat Management Dept, Los Angeles, CA 90089 USA

来源：

ANNUAL REVIEW OF ECONOMICS, VOL 3 | 2011年 / 3卷

基金：

美国国家科学基金会;

关键词：

variable selection; independence screening; oracle properties; penalized likelihood; factor models; portfolio selection; NONCONCAVE PENALIZED LIKELIHOOD; CLIPPED ABSOLUTE DEVIATION; GENERALIZED LINEAR-MODELS; LARGE COVARIANCE MATRICES; VARIABLE SELECTION; ADAPTIVE LASSO; DIVERGING NUMBER; REGRESSION; REGULARIZATION; CONVERGENCE;

D O I：

10.1146/annurev-economics-061109-080451

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

This article reviews the literature on sparse high-dimensional models and discusses some applications in economics and finance. Recent developments in theory, methods, and implementations in penalized least-squares and penalized likelihood methods are highlighted. These variable selection methods are effective in sparse high-dimensional modeling. The limits of dimensionality that regularization methods can handle, the role of penalty functions, and their statistical properties are detailed. Some recent advances in sparse ultra-high-dimensional modeling are also briefly discussed.

引用

页码：291 / 317

页数：27

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