Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/70138
Authors: 
Savin, Ivan
Winker, Peter
Year of Publication: 
2012
Series/Report no.: 
Jena Economic Research Papers 2012,055
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.
Subjects: 
adaptive lasso
elastic net
forecasting
genetic algorithms
heuristic methods
lasso
model selection
JEL: 
C51
C52
C53
C61
C63
Document Type: 
Working Paper

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