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Artificial neural networks versus multivariate statistics: An application from economics

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  • John Cooper
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    Abstract

    An artificial neural network is a computer model that mimics the brain's ability to classify patterns or to make forecasts based on past experience. This paper explains the underlying theory of the widely used back-propagation algorithm and applies this procedure to a problem from the field of international economics, namely the identification of countries that are likely to seek a rescheduling of their international debt-service obligations. A comparison of the results with those obtained from three multivariate statistical procedures applied to the same data set suggests that neural networks are worthy of consideration by the applied economist.

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    File URL: http://www.tandfonline.com/doi/abs/10.1080/02664769921927
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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Journal of Applied Statistics.

    Volume (Year): 26 (1999)
    Issue (Month): 8 ()
    Pages: 909-921

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    Handle: RePEc:taf:japsta:v:26:y:1999:i:8:p:909-921

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    References

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    1. Javeed Nizami, SSAK & Al-Garni, Ahmed Z, 1995. "Forecasting electric energy consumption using neural networks," Energy Policy, Elsevier, vol. 23(12), pages 1097-1104, December.
    2. Kharas, Homi, 1984. "The Long-Run Creditworthiness of Developing Countries: Theory and Practice," The Quarterly Journal of Economics, MIT Press, vol. 99(3), pages 415-39, August.
    3. Feder, Gershon & Just, Richard E., 1977. "A study of debt servicing capacity applying logit analysis," Journal of Development Economics, Elsevier, vol. 4(1), pages 25-38, February.
    4. Frank, Charles Jr. & Cline, William R., 1971. "Measurement of debt servicing capacity: An application of discriminant analysis," Journal of International Economics, Elsevier, vol. 1(3), pages 327-344, August.
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    Cited by:
    1. Roberto Patuelli & Simonetta Longhi & Aura Reggiani & Peter Nijkamp, 2005. "Forecasting Regional Employment in Germany by Means of Neural Networks and Genetic Algorithms," Computational Economics 0511002, EconWPA.
    2. Hiranya K. Nath, 2008. "Country Risk Analysis: A Survey of the Quantitative Methods," Working Papers 0804, Sam Houston State University, Department of Economics and International Business.
    3. Yochanan Shachmurove & Doris Witkowska, . "Utilizing Artificial Neural Network Model to Predict Stock Markets," Penn CARESS Working Papers cae679cdc2e020f74d692ae73, Penn Economics Department.

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