Lasso–type and Heuristic Strategies in Model Selection and Forecasting

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 285)

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

False Negative Rate True Positive Rate Heuristic Strategy Adaptive Lasso Leading Indicator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.DFG Research Training Program ‘The Economics of Innovative Change’Friedrich Schiller University Jena and Max Planck Institute of EconomicsJenaGermany
  2. 2.Justus Liebig University GiessenGiessenGermany
  3. 3.Centre for European Economic ResearchMannheimGermany

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