Model selection via genetic algorithms illustrated with cross-country growth data |
| |
Authors: | Eduardo Acosta-González Fernando Fernández-Rodríguez |
| |
Institution: | (1) Department of Quantitative Methods, Campus de Tafira, Facultad de CC. Económicas, University of Las Palmas de Gran Canaria, 35017 Las Palmas de G.C., Spain |
| |
Abstract: | We provide a new simple procedure for selecting econometric models, which is used to select the regressors of the cross-country
growth model regression. This procedure is based on a heuristic approach called genetic algorithms which are used to explore
the universe of models made available by a General Unrestricted Model. This search process of the correct model is only guided
by the Schwarz information criterion, which acts as the loss function of the genetic algorithm in order to rank the models.
Our procedure shows good performance relative to other alternative methodologies when they are compared in a simulation environment.
This research was supported by the Spanish Ministry of Science and Education through the project SEJ2006-07701. |
| |
Keywords: | Growth Genetic algorithms Data mining Regressors selection |
本文献已被 SpringerLink 等数据库收录! |