Sparse High-Dimensional Models in Economics

Jianqing Fan, Jinchi Lv () and Lei Qi ()
Additional contact information
Jinchi Lv: Information and Operations Management Department, Marshall School of Business, University of Southern California, Los Angeles, California 90089
Lei Qi: Bendheim Center for Finance and Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey 08544

Annual Review of Economics, 2011, vol. 3, issue 1, 291-317

Abstract: 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.

Keywords: variable selection; independence screening; oracle properties; penalized likelihood; factor models; portfolio selection (search for similar items in EconPapers)
JEL-codes: C13 C51 (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (62)

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