Artificial neural networks versus multivariate statistics: An application from economics
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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Volume (Year): 26 (1999)
Issue (Month): 8 ()
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- 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.
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- Homi Kharas, 1984. "The Long-Run Creditworthiness of Developing Countries: Theory and Practice," The Quarterly Journal of Economics, Oxford University Press, vol. 99(3), pages 415-439.
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