Giovanis, Eleftherios (2008): NeuroFuzzy approach for the predictions of economic crisis.

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Abstract
In this paper we present the neurofuzzy 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.
Item Type:  MPRA Paper 

Original Title:  NeuroFuzzy approach for the predictions of economic crisis 
Language:  English 
Keywords:  Economic crisis; ANFIS; NeuroFuzzy, fuzzy rules; triangle function; Gaussian function; Generalized Bell function forecasting; discrete choice models; Logit; Probit; economy of USA; MATLAB 
Subjects:  C  Mathematical and Quantitative Methods > C5  Econometric Modeling > C53  Forecasting and Prediction Methods; Simulation Methods C  Mathematical and Quantitative Methods > C6  Mathematical Methods; Programming Models; Mathematical and Simulation Modeling > C63  Computational Techniques; Simulation Modeling C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics > C45  Neural Networks and Related Topics C  Mathematical and Quantitative Methods > C2  Single Equation Models; Single Variables > C25  Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions 
Item ID:  24656 
Depositing User:  Eleftherios Giovanis 
Date Deposited:  28. Aug 2010 16:52 
Last Modified:  15. Feb 2013 10:45 
References:  Coats, P.K., and Fant, F. L. (1993). Recognizing financial distress patterns using a neural network tool, Financial Management, Vol. 22, pp. 142155 Demirguckunt, A., and Detragiache, E. (1998). The Determinants of Banking Crises in Developing and Developed Countries. IMF Staff Papers, Vol. 45, No. 1, pp. 81109 Eichengreen, B., and Rose, A.K. (1998). Staying Afloat When the Wind Shifts: External Factors and EmergingMarket Banking Crises. NBER Working Papers 6370. National Bureau of Economic Research, Cambridge, MA Frankel, J., and Rose, A.K. (1996). Currency Crashes in emerging Markets: An Empirical Treatment, International Finance Discussion Papers 534. Board of Governors of the Federal Reserve System. Washington. D.C Greene, W.H. (2008). Econometric Analysis, Sixth Edition, Prentice Hall: New Jersey Jang, J.S.R. (1993). ANFIS: AdaptiveNetworkbased Fuzzy Inference Systems. IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3, pp. 665685 Jang, J.S. R. and Sun, C.T. (1995). Neurofuzzy Modeling and Control. Proceedings of the IEEE, Vol. 83, No. 3, pp. 378406, March Kaminsky, L.G., and Reinhart, C. M. (1996). The Twin Crises: The Causes of Banking and Balance of Payments Problems. Federal Reserve Board Discussion Papers 544. Board of Governors of the Federal Reserve System.Washington. D.C. Kaminsky, L.G., Lizondo, S., and Reinhart, C.M. (1998). Leading Indicators of Currency Crises, IMF Staff Papers, Vol. 45, No. 1, pp. 148 Moore, E. H. (1920). On the reciprocal of the general algebraic matrix. Bulletin of the American Mathematical Society, Vol. 26, pp. 394–395 Nachev. A, and Stoyanov, B. (2007). A Default ARTMAP Neural Networks for Financial Diagnosis. Proceedings of the 2007 International Conference on Data Mining, DMIN 2007, June 2528, Las Vegas, Nevada, USA. CSREA Petrou, M. and Bosdogianni, P. (2000). Image Processing: The Fundamentals, John Wiley Penrose, R. (1955). A generalized inverse for matrices. Proceedings of the Cambridge Philosophical Society, Vol. 51, pp. 406–413 Zhang, G. Hu, Y., and Patuwo, E.B. (1999). Artificial neural networks in bankruptcy prediction: General framework and crossvalidation analysis, European Journal of Operation Research, Vol. 116, pp. 1632. 
URI:  http://mpra.ub.unimuenchen.de/id/eprint/24656 