Sparse High Dimensional Models in Economics
53 Pages Posted: 16 Aug 2010
Date Written: August 15, 2010
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
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