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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bai, J., Ng, S.: Forecasting economic time series using targeted predictors. Journal of Econometrics 146(2), 304–317 (2008)
Candès, E.J., Tao, T.: The Dantzig selector: statistical estimation when p is much larger than n. Annals of Statistics 35(6), 2313–2351 (2007)
Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. Annals of Statistics 32, 407–489 (2004)
Frank, I.E., Friedman, J.H.: A statistical view of some chemometrics regression tools. Technometrics 35(2), 109–135 (1993)
Gasso, G., Rakotomamonjy, A., Canu, S.: Recovering sparse signals with a certain family of non-convex penalties and DC programming. IEEE Trans. on Signal Processing 57(12), 4686–4698 (2009)
Gilli, M., Winker, P.: Heuristic optimization methods in econometrics. In: Belsley, D., Kontoghiorghes, E. (eds.) Handbook of Computational Econometrics, pp. 81–119. Wiley, Chichester (2009)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York (2009)
Hendry, D.F., Krolzig, H.M.: The properties of automatic ’GETS’ modelling. The Economic Journal 115(502), C32–C61 (2005)
Kapetanios, G., Labhard, V., Price, S.: Forecasting using Bayesian and information-theoretic model averaging: An application to U.K. inflation. Journal of Business & Economic Statistics 26(1), 33–41 (2008)
Meinshausen, N., Yu, B.: Lasso-type recovery of sparse representations for high-dimensional data. Annals of Statistics 37(1), 246–270 (2008)
Ozyldrim, A., Schaitkin, B., Zarnowitz, V.: Business cycles in the Euro area defined with coincident economic indicators and predicted with leading economic indicators. Journal of Forecasting 29(1–2), 6–28 (2010)
Perez-Amaral, T., Gallo, G.M., White, H.: A flexible tool for model building: The relevant transformation of the inputs network approach (RETINA). Oxford Bulletin of Economics and Statistics 65(1), 821–838 (2003)
Savin, I.: A comparative study of the lasso–type and heuristic model selection methods. COMISEF Working Paper Series 42 (2010)
Savin, I., Winker, P.: Heuristic optimization methods for dynamic panel data model selection. Application on the Russian innovative performance. Computational Economics (forthcoming)
Savin, I., Winker, P.: Heuristic model selection for leading indicators in Russia and Germany. MAGKS Joint Discussion Paper Series in Economics (January 2011)
Tibshirani, R.: Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society B 58(1), 267–288 (1996)
Vogt, G.: The forecasting performance of ifo-indicators under realtime conditions. Journal of Economics and Statistics 227(1), 87–101 (2007)
Winker, P.: Identification of multivariate AR-models by threshold accepting. Computational Statistics & Data Analysis 20(3), 295–307 (1995)
Zhao, P., Yu, B.: On model selection consistency of lasso. Journal of Machine Learning Research 7, 2541–2563 (2006)
Zou, H.: The adaptive lasso and its oracle properties. Journal of the American Statistical Association 101, 1418–1429 (2006)
Zou, H., Hastie, T.: Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society B 67(2), 301–320 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag GmbH Berlin Heidelberg
About this chapter
Cite this chapter
Savin, I., Winker, P. (2013). Lasso–type and Heuristic Strategies in Model Selection and Forecasting. In: Borgelt, C., Gil, M., Sousa, J., Verleysen, M. (eds) Towards Advanced Data Analysis by Combining Soft Computing and Statistics. Studies in Fuzziness and Soft Computing, vol 285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30278-7_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-30278-7_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30277-0
Online ISBN: 978-3-642-30278-7
eBook Packages: EngineeringEngineering (R0)