Lasso-type and Heuristic Strategies in Model Selection and Forecasting

Ivan Savin and Peter Winker

No 2012-055, Jena Economics Research Papers from Friedrich-Schiller-University Jena

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.

Keywords: Adaptive Lasso; Elastic net; Forecasting; Genetic algorithms; Heuristic methods; Lasso; Model selection (search for similar items in EconPapers)
JEL-codes: C51 C52 C53 C61 C63 (search for similar items in EconPapers)
Date: 2012-10-11
New Economics Papers: this item is included in nep-cis, nep-cmp, nep-ecm, nep-for and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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