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Model selection in neural networks

Author

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  • Anders, Ulrich
  • Korn, Olaf

Abstract

In this article we examine how model selection in neural networks can be guided by statistical procedures such as hypotheses tests, information criteria and cross validation. The application of these methods in neural network models is discussed, paying attention especially to the identification problems encountered. We then propose five specification strategies based on different statistical procedures and compare them in a simulation study. As the results of the study are promising, it is suggested that a statistical analysis should become an integral part of neural network modelling.

Suggested Citation

  • Anders, Ulrich & Korn, Olaf, 1996. "Model selection in neural networks," ZEW Discussion Papers 96-21, ZEW - Leibniz Centre for European Economic Research.
    • Handle: RePEc:zbw:zewdip:9621
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    Citations

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    Cited by:

    1. Andrea Bucci, 2020. "Realized Volatility Forecasting with Neural Networks," Journal of Financial Econometrics, Oxford University Press, vol. 18(3), pages 502-531.
    2. Steven M. Ramsey & Jason S. Bergtold, 2021. "Examining Inferences from Neural Network Estimators of Binary Choice Processes: Marginal Effects, and Willingness-to-Pay," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 1137-1165, December.
    3. Ulrich Anders & Andrea Szczesny, 1998. "Prognose von Insolvenzwahrscheinlichkeiten mit Hilfe logistischer neuronaler Netzwerke," Schmalenbach Journal of Business Research, Springer, vol. 50(10), pages 892-915, October.
    4. Anders, Ulrich & Korn, Olaf & Schmitt, Christian, 1996. "Improving the pricing of options: a neural network approach," ZEW Discussion Papers 96-04, ZEW - Leibniz Centre for European Economic Research.

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