Lasso-type and Heuristic Strategies in Model Selection and Forecasting
AbstractSeveral 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 InfoPaper provided by Friedrich-Schiller-University Jena, Max-Planck-Institute of Economics in its series Jena Economic Research Papers with number 2012-055.
Date of creation: 11 Oct 2012
Date of revision:
Adaptive Lasso; Elastic net; Forecasting; Genetic algorithms; Heuristic methods; Lasso; Model selection;
Find related papers by JEL classification:
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-10-20 (All new papers)
- NEP-CIS-2012-10-20 (Confederation of Independent States)
- NEP-CMP-2012-10-20 (Computational Economics)
- NEP-ECM-2012-10-20 (Econometrics)
- NEP-FOR-2012-10-20 (Forecasting)
- NEP-ORE-2012-10-20 (Operations Research)
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- Winker, Peter, 1995. "Identification of multivariate AR-models by threshold accepting," Computational Statistics & Data Analysis, Elsevier, vol. 20(3), pages 295-307, September.
- Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
- Ivan Savin, 2010.
"A comparative study of the Lasso-type and heuristic model selection methods,"
- Ivan Savin, 2013. "A Comparative Study of the Lasso-type and Heuristic Model Selection Methods," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), Justus-Liebig University Giessen, Department of Statistics and Economics, vol. 233(4), pages 526-549, July.
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