Lasso-type and heuristic strategies in model selection and forecasting

Ivan Savin, Peter Winker

Research output: Contribution to journalReview articleResearchpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)165-176
Number of pages12
JournalStudies in Fuzziness and Soft Computing
Volume285
DOIs
Publication statusPublished - 2013

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