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

Listed author(s):
  • Daniel Ramirez A.
  • Juan M. Gómez G.
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    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

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    Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2004 with number 246.

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    Date of creation: 11 Aug 2004
    Handle: RePEc:sce:scecf4:246
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