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Neuro-Fuzzy approach for the predictions of economic crisis

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  • Giovanis, Eleftherios

Abstract

In this paper we present the neuro-fuzzy technology for the prediction of economic crisis of USA economy. Our findings support ANFIS models to traditional discrete choice models of Probit and Logit, indicating that the last models are not very useful for forecasting purposes. We have developed a MATLAB routine to show how ANFIS procedure works and it is provided for replications, further research applications and experiments, for modifications, expansions and improvements. We propose the use of both models, because with discrete choice models we can examine and investigate the effects of the inputs or the independent variables, while we can simultaneously use ANFIS for forecasting purposes. The wise option and the most appropriate scientific action is to combine both models and not taking only one of them.

Suggested Citation

  • Giovanis, Eleftherios, 2008. "Neuro-Fuzzy approach for the predictions of economic crisis," MPRA Paper 24656, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:24656
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    References listed on IDEAS

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    1. Zhang, Guoqiang & Y. Hu, Michael & Eddy Patuwo, B. & C. Indro, Daniel, 1999. "Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis," European Journal of Operational Research, Elsevier, vol. 116(1), pages 16-32, July.
    2. Carmen M. Reinhart & Graciela L. Kaminsky, 1999. "The Twin Crises: The Causes of Banking and Balance-of-Payments Problems," American Economic Review, American Economic Association, vol. 89(3), pages 473-500, June.
    3. Pamela K. Coats & L. Franklin Fant, 1993. "Recognizing Financial Distress Patterns Using a Neural Network Tool," Financial Management, Financial Management Association, vol. 22(3), Fall.
    4. Graciela Kaminsky & Saul Lizondo & Carmen M. Reinhart, 1998. "Leading Indicators of Currency Crises," IMF Staff Papers, Palgrave Macmillan, vol. 45(1), pages 1-48, March.
    5. Barry Eichengreen & Andrew K. Rose, 1998. "Staying Afloat When the Wind Shifts: External Factors and Emerging-Market Banking Crises," NBER Working Papers 6370, National Bureau of Economic Research, Inc.
    6. Frankel, Jeffrey A. & Rose, Andrew K., 1996. "Currency crashes in emerging markets: An empirical treatment," Journal of International Economics, Elsevier, vol. 41(3-4), pages 351-366, November.
    7. Asli Demirgüç-Kunt & Enrica Detragiache, 1998. "The Determinants of Banking Crises in Developing and Developed Countries," IMF Staff Papers, Palgrave Macmillan, vol. 45(1), pages 81-109, March.
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    More about this item

    Keywords

    Economic crisis; ANFIS; Neuro-Fuzzy; fuzzy rules; triangle function; Gaussian function; Generalized Bell function forecasting; discrete choice models; Logit; Probit; economy of USA; MATLAB;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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