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Sparse High-Dimensional Models in Economics

Author

Listed:
  • Jianqing Fan

    (Bendheim Center for Finance and Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey 08544)

  • 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)

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.

Suggested Citation

  • Jianqing Fan & Jinchi Lv & Lei Qi, 2011. "Sparse High-Dimensional Models in Economics," Annual Review of Economics, Annual Reviews, vol. 3(1), pages 291-317, September.
    • Handle: RePEc:anr:reveco:v:3:y:2011:p:291-317
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    File URL: http://www.annualreviews.org/doi/abs/10.1146/annurev-economics-061109-080451
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    More about this item

    Keywords

    variable selection; independence screening; oracle properties; penalized likelihood; factor models; portfolio selection;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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