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Economic classification and regression problems and neural networks

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  • Arnošt VESELÝ

    (Department of Information Engineering, Faculty of Economics and Management, Czech University of Life Sciences, Prague, Czech Republic)

Abstract

Artificial neural networks provide powerful models for solving many economic classifications, as well as regression problems. For example, they were successfully used for the discrimination between healthy economic agents and those prone to bankruptcy, for the inflation-deflation forecasting, for the currency exchange rates prediction, or for the prediction of share prices. At present, the neural models are part of the majority of standard statistical software packages. This paper discusses the basic principles, which the neural network models are based on, and sum up the important principles that must be respected in order that their utilization in practice is efficient.

Suggested Citation

  • Arnošt VESELÝ, 2011. "Economic classification and regression problems and neural networks," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 57(3), pages 150-157.
    • Handle: RePEc:caa:jnlage:v:57:y:2011:i:3:id:50-2010-agricecon
    DOI: 10.17221/50/2010-AGRICECON
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    References listed on IDEAS

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    1. Ahmed Emam & Hokey Min, 2009. "The artificial neural network for forecasting foreign exchange rates," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 5(6), pages 740-757.
    2. Nakamura, Emi, 2005. "Inflation forecasting using a neural network," Economics Letters, Elsevier, vol. 86(3), pages 373-378, March.
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    Cited by:

    1. Jiří LÝSEK & Jiří ŠŤASTNÝ, 2014. "Automatic discovery of the regression model by the means of grammatical and differential evolution," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 60(12), pages 546-552.

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