- A-Z
- Jena Economic Resea...
- Volume 6
- Lasso-type and Heur...
- Abgebildete Person
- Erschienen
- 11. Oktober 2012
- Nummer des Discussion-Papers
-
2012-055
- Schlagwort(e)
-
Adaptive Lasso
Elastic net
Forecasting
Genetic algorithms
Heuristic Methods
Lasso
Model selection
- Zusammenfsg.
-
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.
- article pub. typess JER
- Research article
- article languages JER
- Englisch
- JEL-Classification for JER
- C51 - Model Construction and Estimation ; C52 - Model Evaluation and Selection ; C53 - Forecasting and Other Model Applications ; C61 - Optimization Techniques; Programming Models; Dynamic Analysis ; C63 - Computational Techniques