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Artificial neural networks versus multivariate statistics: An application from economics

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  • John Cooper

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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  • John Cooper, 1999. "Artificial neural networks versus multivariate statistics: An application from economics," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(8), pages 909-921.
    • Handle: RePEc:taf:japsta:v:26:y:1999:i:8:p:909-921
    DOI: 10.1080/02664769921927
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    Cited by:

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    2. 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.
    3. Tamás Kristóf, 2021. "Sovereign Default Forecasting in the Era of the COVID-19 Crisis," JRFM, MDPI, vol. 14(10), pages 1-24, October.
    4. Dan Farhat, 2014. "Artificial Neural Networks and Aggregate Consumption Patterns in New Zealand:," Working Papers 1404, University of Otago, Department of Economics, revised Mar 2014.
    5. Lean Yu & Xinxie Li & Ling Tang & Zongyi Zhang & Gang Kou, 2015. "Social credit: a comprehensive literature review," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 1(1), pages 1-18, December.
    6. Yochanan Shachmurove & Doris Witkowska, "undated". "Utilizing Artificial Neural Network Model to Predict Stock Markets," Penn CARESS Working Papers cae679cdc2e020f74d692ae73, Penn Economics Department.
    7. Suellen Teixeira Zavadzki de Pauli & Mariana Kleina & Wagner Hugo Bonat, 2020. "Comparing Artificial Neural Network Architectures for Brazilian Stock Market Prediction," Annals of Data Science, Springer, vol. 7(4), pages 613-628, December.
    8. Nath, Hiranya K., 2009. "Country Risk Analysis: A Survey of the Quantitative Methods," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 62(1), pages 69-94.
    9. Roberto Patuelli & Aura Reggiani & Peter Nijkamp & Uwe Blien, 2006. "New Neural Network Methods for Forecasting Regional Employment: an Analysis of German Labour Markets," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(1), pages 7-30.
    10. M. H. Lee & H. J. Sadaei & Suhartono, 2013. "Improving TAIEX forecasting using fuzzy time series with Box--Cox power transformation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(11), pages 2407-2422, November.
    11. Daniela Carlucci & Paolo Renna & Giovanni Schiuma, 2013. "Evaluating service quality dimensions as antecedents to outpatient satisfaction using back propagation neural network," Health Care Management Science, Springer, vol. 16(1), pages 37-44, March.
    12. Roberto Patuelli & Peter Nijkamp & Simonetta Longhi & Aura Reggiani, 2008. "Neural Networks and Genetic Algorithms as Forecasting Tools: A Case Study on German Regions," Environment and Planning B, , vol. 35(4), pages 701-722, August.
    13. 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.
    14. Dan 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.

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