Model Selection Using Information Criteria and Genetic Algorithms

Authors:
Kelvin G. Balcombe
Affiliations:
Imperial College, Wye Campus, London, U.K.
Venue:
Computational Economics
Year:
2005

Citing 2
Cited 1

Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
An analysis of the behavior of a class of genetic adaptive systems.

An analysis of the behavior of a class of genetic adaptive systems.

Predicting EU Energy Industry Excess Returns on EU Market Index via a Constrained Genetic Algorithm

Computational Economics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Automated model searches using information criteria are used for the estimation of linear single equation models. Genetic algorithms are described and used for this purpose. These algorithms are shown to be a practical method for model selection when the number of sub-models are very large. Several examples are presented including tests for bivariate Granger causality and seasonal unit roots. Automated selection of an autoregressive distributed lag model for the consumption function in the US is also undertaken.