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A note on cointegrated relationships estimated with genetic algorithms

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  • Mariano Matilla-Garcia

Abstract

Estimation techniques based on Genetic Algorithms (GA) have been studied in the presence of cointegrated variables. Several applications of GA to time-series have ignored the fact that the equation estimated by GA might be spurious. In this line, in this study it is shown that: (1) GA robustly detects this kind of relationship when the process contains a linear cointegrated relationship, (2) estimated models provide real fitness instead of spurious fitness. The well known cointegrated relation between income and consumption is estimated using GA.

Suggested Citation

  • Mariano Matilla-Garcia, 2005. "A note on cointegrated relationships estimated with genetic algorithms," Applied Economics Letters, Taylor & Francis Journals, vol. 12(4), pages 235-238.
    • Handle: RePEc:taf:apeclt:v:12:y:2005:i:4:p:235-238
    DOI: 10.1080/1350485042000329112
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    References listed on IDEAS

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    1. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
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    3. Beenstock, Michael & Szpiro, George, 2002. "Specification search in nonlinear time-series models using the genetic algorithm," Journal of Economic Dynamics and Control, Elsevier, vol. 26(5), pages 811-835, May.
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