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.
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- Bai, Jushan & Ng, Serena, 2008. "Forecasting economic time series using targeted predictors," Journal of Econometrics, Elsevier, vol. 146(2), pages 304-317, October.
- Winker, Peter, 1995.
"Identification of multivariate AR-models by threshold accepting,"
Computational Statistics & Data Analysis,
Elsevier, vol. 20(3), pages 295-307, September.
- Winker, Peter, 1994. "Identification of multivariate AR-models by threshold accepting," Discussion Papers, Series II 224, University of Konstanz, Collaborative Research Centre (SFB) 178 "Internationalization of the Economy".