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A simple strategy to prune neural networks with an application to economic time series

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  • Kaashoek, J.F.
  • van Dijk, H.K.

Abstract

A major problem in applying neural networks is specifying the size of the network. Even for moderately sized networks the number of parameters may become large compared to the number of data. In this paper network performance is examined while reducing the size of the network through the use of multiple correlation coefficients, principal component analysis of residuals and graphical analysis of network output per hidden layer cell and input layer cell.

Suggested Citation

  • Kaashoek, J.F. & van Dijk, H.K., 1998. "A simple strategy to prune neural networks with an application to economic time series," Econometric Institute Research Papers EI 9854, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:1523
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    1. Schotman, Peter C & van Dijk, Herman K, 1991. "On Bayesian Routes to Unit Roots," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(4), pages 387-401, Oct.-Dec..
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    Cited by:

    1. Kaashoek, J.F. & van Dijk, H.K., 1999. "Neural network analysis of varying trends in real exchange rates," Econometric Institute Research Papers EI 9915-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

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    Keywords

    economic time series; neural networks;

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