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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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    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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    1. 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.
    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. 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.
    5. Pierre Dhonte, 1975. "Describing External Debt Situations: A Roll-over Approach (La description de l'endettement extérieur: l'approche du refinancement) (La refinanciación y su utilidad para la descripción de situ," IMF Staff Papers, Palgrave Macmillan, vol. 22(1), pages 159-186, March.
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    Cited by:
    1. 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.
    2. 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.
    3. Malik, Farooq & Nasereddin, Mahdi, 2006. "Forecasting output using oil prices: A cascaded artificial neural network approach," Journal of Economics and Business, Elsevier, vol. 58(2), pages 168-180.
    4. Daniel Farhat, 2014. "Information Processing, Pattern Transmission and Aggregate Consumption Patterns in New Zealand," Working Papers 1405, University of Otago, Department of Economics, revised Mar 2014.
    5. Yochanan Shachmurove & Doris Witkowska, . "Utilizing Artificial Neural Network Model to Predict Stock Markets," Penn CARESS Working Papers cae679cdc2e020f74d692ae73, Penn Economics Department.
    6. Dan Farhat, 2012. "Artificial Neural Networks and Aggregate Consumption Patterns in New Zealand," Working Papers 1205, University of Otago, Department of Economics, revised Dec 2012.
    7. Daniel Farhat, 2014. "Artificial Neural Networks and Aggregate Consumption Patterns in New Zealand," Working Papers 1404, University of Otago, Department of Economics, revised Mar 2014.

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