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Lasso-type and Heuristic Strategies in Model Selection and Forecasting

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  • Ivan Savin

    ()
    (DFG Research Training Program "The Economics of Innovative Change", Friedrich Schiller University Jena and Max Planck Institute of Economics)

  • Peter Winker

    ()
    (Justus Liebig University Giessen, and Centre for European Economic Research, Mannheim)

Abstract

Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to apply a regularization strategy and solve the model selection task as a continuous optimization problem. One of the most popular approaches in this research field is given by Lasso-type methods. An alternative approach is based on information criteria. In contrast to the Lasso, these methods also work well in the case of highly correlated predictors. However, this performance can be impaired by the only asymptotic consistency of the information criteria. The resulting discrete optimization problems exhibit a high computational complexity. Therefore, a heuristic optimization approach (Genetic Algorithm) is applied. The two strategies are compared by means of a Monte-Carlo simulation study together with an empirical application to leading business cycle indicators in Russia and Germany.

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Bibliographic Info

Paper provided by Friedrich-Schiller-University Jena, Max-Planck-Institute of Economics in its series Jena Economic Research Papers with number 2012-055.

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Date of creation: 11 Oct 2012
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Handle: RePEc:jrp:jrpwrp:2012-055

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Related research

Keywords: Adaptive Lasso; Elastic net; Forecasting; Genetic algorithms; Heuristic methods; Lasso; Model selection;

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  1. Winker, Peter, 1995. "Identification of multivariate AR-models by threshold accepting," Computational Statistics & Data Analysis, Elsevier, vol. 20(3), pages 295-307, September.
  2. Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
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Cited by:
  1. Ivan Savin, 2010. "A comparative study of the Lasso-type and heuristic model selection methods," Working Papers 042, COMISEF.

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