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Choosing Variables With A Genetic Algorithm For Econometric Models Based On Neural Networks Learning And Adaptation

Author

Listed:
  • Daniel Ramirez A.
  • Juan M. Gómez G.

Abstract

The mixture of two already known soft computing technics, like Genetic Algorithms and Neural Networks (NN) in Financial modeling, takes a new approach in the search for the best variables involving an Econometric model using a Neural Network. This new approach helps to recognice the importance of an economic variable among different variables regarding econometric modeling. A Genetic algorithm constructs a set of working neural networks, evolving the inputs given to each NN as well as its internal arquitecture. An input subset is chosen by the genetic algorithm from a multiple variable set, due to the NN training results from this given input. At the end of the evolutionary process, the best given inputs for an especific neural network arquitecture are obtained. The experimental results revealed an improvement of 80% in the NN learning performace of the Econometric model. At the same time it reduces the model complexity by 46%, runing the evolutionary process on a PC without large computer resources

Suggested Citation

  • Daniel Ramirez A. & Juan M. Gómez G., 2004. "Choosing Variables With A Genetic Algorithm For Econometric Models Based On Neural Networks Learning And Adaptation," Computing in Economics and Finance 2004 246, Society for Computational Economics.
  • Handle: RePEc:sce:scecf4:246
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    More about this item

    Keywords

    Neural Networks; Genetic Algorithms; Econometric Modeling;

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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