Sparse High Dimensional Models in Economics

53 Pages Posted: 16 Aug 2010

See all articles by Jianqing Fan

Jianqing Fan

Princeton University - Bendheim Center for Finance

Jinchi Lv

University of Southern California - Marshall School of Business

Lei Qi

Princeton University - Bendheim Center for Finance

Multiple version iconThere are 2 versions of this paper

Date Written: August 15, 2010

Abstract

This paper reviews the literature on sparse high dimensional models and discusses some applications in economics and finance. Recent developments of theory, methods, and implementations in penalized least squares and penalized likelihood methods are highlighted. These variable selection methods are proved to be effective in high dimensional sparse 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 ultra-high dimensional sparse modeling are also briefly discussed.

Keywords: Variable selection, independence screening, sparsity, oracle properties, penalized least squares, penalized likelihood, spurious correlation, sparse VAR, factor models, volatility estimation, portfolio selection

JEL Classification: C13, C32, C33, C52

Suggested Citation

Fan, Jianqing and Lv, Jinchi and Qi, Lei, Sparse High Dimensional Models in Economics (August 15, 2010). Available at SSRN: https://ssrn.com/abstract=1659322 or http://dx.doi.org/10.2139/ssrn.1659322

Jianqing Fan

Princeton University - Bendheim Center for Finance ( email )

26 Prospect Avenue
Princeton, NJ 08540
United States
609-258-7924 (Phone)
609-258-8551 (Fax)

HOME PAGE: http://orfe.princeton.edu/~jqfan/

Jinchi Lv

University of Southern California - Marshall School of Business ( email )

701 Exposition Blvd
Los Angeles, CA 90089
United States

HOME PAGE: http://www-rcf.usc.edu/~jinchilv

Lei Qi (Contact Author)

Princeton University - Bendheim Center for Finance ( email )

26 Prospect Avenue
Princeton, NJ 08540
United States

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