Lasso-Type and Heuristic Strategies in Model Selection and Forecasting

Jena Economic Research Papers 2012-055

14 Pages Posted: 15 Oct 2012

See all articles by Ivan Savin

Ivan Savin

Ural Federal University; Autonomous University of Barcelona

Peter Winker

University of Giessen - Department of Economics

Date Written: October 11, 2012

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

JEL Classification: C51, C52, C53, C61, C63

Suggested Citation

Savin, Ivan and Winker, Peter, Lasso-Type and Heuristic Strategies in Model Selection and Forecasting (October 11, 2012). Jena Economic Research Papers 2012-055. Available at SSRN: https://ssrn.com/abstract=2161793

Ivan Savin (Contact Author)

Ural Federal University ( email )

Yekaterinburg
Russia

Autonomous University of Barcelona ( email )

Plaça Cívica
Cerdañola del Valles
Barcelona, Barcelona 08193
Spain

Peter Winker

University of Giessen - Department of Economics ( email )

Licher Str. 62
D-35394 Giessen, DE
Germany

HOME PAGE: http://wiwi.uni-giessen.de/home/oekonometrie/

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