Sparse high-dimensional models in economics

Jianqing Fan, Jinchi Lv, Lei Qi

Research output: Contribution to journalReview articlepeer-review

121 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)291-317
Number of pages27
JournalAnnual Review of Economics
StatePublished - 2011

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics


  • Factor models
  • Independence screening
  • Oracle properties
  • Penalized likelihood
  • Portfolio selection
  • Variable selection


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