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 ; Probabilities 
Item ID:  24656 
Depositing User:  Eleftherios Giovanis 
Date Deposited:  28 Aug 2010 16:52 
Last Modified:  28 Sep 2019 06:47 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/24656 