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Model Selection Using Information Criteria and Genetic Algorithms

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Listed author(s):
  • Kelvin Balcombe

    ()

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. Copyright Springer Science + Business Media, Inc. 2005

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File URL: http://hdl.handle.net/10.1007/s10614-005-2209-8
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Article provided by Springer & Society for Computational Economics in its journal Computational Economics.

Volume (Year): 25 (2005)
Issue (Month): 3 (June)
Pages: 207-228

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Handle: RePEc:kap:compec:v:25:y:2005:i:3:p:207-228
DOI: 10.1007/s10614-005-2209-8
Contact details of provider: Web page: http://www.springer.com

Web page: http://www.comp-econ.org/

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Order Information: Web: http://www.springer.com/economics/economic+theory/journal/10614/PS2

Keywords: algorithms; autoregressive; distributed lags; genetic; information criteria; model selection;

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Cited by:

  1. Massimiliano Kaucic, 2009. "Predicting EU Energy Industry Excess Returns on EU Market Index via a Constrained Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 34(2), pages 173-193, September.

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