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Model Selection Using Information Criteria and Genetic Algorithms

  • Kelvin Balcombe

    ()

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 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
Contact details of provider: Web page: http://www.springerlink.com/link.asp?id=100248
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  1. Carmen Fernandez & E Ley & Mark F J Steel, 2004. "Benchmark priors for Bayesian models averaging," ESE Discussion Papers 66, Edinburgh School of Economics, University of Edinburgh.
  2. Joseph Beaulieu, J. & Miron, Jeffrey A., 1993. "Seasonal unit roots in aggregate U.S. data," Journal of Econometrics, Elsevier, vol. 55(1-2), pages 305-328.
  3. Smith, M. & Kohn, R., . "Nonparametric Regression using Bayesian Variable Selection," Statistics Working Paper _009, Australian Graduate School of Management.
  4. Peter C.B. Phillips, 1992. "Bayesian Model Selection and Prediction with Empirical Applications," Cowles Foundation Discussion Papers 1023, Cowles Foundation for Research in Economics, Yale University.
  5. Magnus, Jan R & Morgan, Mary S, 1997. "The Data: A Brief Description," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(5), pages 651-61, Sept.-Oct.
  6. Phillips, Peter C. B., 1995. "Bayesian prediction a response," Journal of Econometrics, Elsevier, vol. 69(1), pages 351-365, September.
  7. Magnus, J.R. & Morgan, M.S., 1997. "The data : A brief description," Other publications TiSEM 4bdd1a8c-adbb-4786-9fc1-b, School of Economics and Management.
  8. Hylleberg, S. & Engle, R.F. & Granger, C.W.J. & Yoo, B.S., 1988. "Seasonal, Integration And Cointegration," Papers 6-88-2, Pennsylvania State - Department of Economics.
  9. Magnus, Jan R & Morgan, Mary S, 1997. "Design of the Experiment," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(5), pages 459-65, Sept.-Oct.
  10. Chao, John C. & Phillips, Peter C. B., 1999. "Model selection in partially nonstationary vector autoregressive processes with reduced rank structure," Journal of Econometrics, Elsevier, vol. 91(2), pages 227-271, August.
  11. de Crombrugghe, Denis & Palm, Franz C & Urbain, Jean-Pierre, 1997. "Statistical Demand Functions for Food in the USA and the Netherlands," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(5), pages 615-37, Sept.-Oct.
  12. Werner Ploberger & Peter C.B. Phillips, 1998. "Rissanen's Theorem and Econometric Time Series," Cowles Foundation Discussion Papers 1197, Cowles Foundation for Research in Economics, Yale University.
  13. Kelvin Balcombe & Alastair Bailey & Iain Fraser, 2005. "Measuring the impact of R&D on Productivity from a Econometric Time Series Perspective," Journal of Productivity Analysis, Springer, vol. 24(1), pages 49-72, 09.
  14. Werner Ploberger & Peter C. B. Phillips, 2003. "Empirical Limits for Time Series Econometric Models," Econometrica, Econometric Society, vol. 71(2), pages 627-673, March.
  15. repec:ner:tilbur:urn:nbn:nl:ui:12-74211 is not listed on IDEAS
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