Lasso–type and Heuristic Strategies in Model Selection and Forecasting
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.
KeywordsFalse Negative Rate True Positive Rate Heuristic Strategy Adaptive Lasso Leading Indicator
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- 13.Savin, I.: A comparative study of the lasso–type and heuristic model selection methods. COMISEF Working Paper Series 42 (2010)Google Scholar
- 14.Savin, I., Winker, P.: Heuristic optimization methods for dynamic panel data model selection. Application on the Russian innovative performance. Computational Economics (forthcoming)Google Scholar
- 15.Savin, I., Winker, P.: Heuristic model selection for leading indicators in Russia and Germany. MAGKS Joint Discussion Paper Series in Economics (January 2011)Google Scholar
- 17.Vogt, G.: The forecasting performance of ifo-indicators under realtime conditions. Journal of Economics and Statistics 227(1), 87–101 (2007)Google Scholar