Giovanis, Eleftherios (2012): Study of Discrete Choice Models and Adaptive NeuroFuzzy Inference System in the Prediction of Economic Crisis Periods in USA. Published in: Economic Analysis & Policy , Vol. 42, No. 1 (March 2012): pp. 7995.

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Abstract
In this study two approaches are applied for the prediction of the economic recession or expansion periods in USA. The first approach includes Logit and Probit models and the second is an Adaptive NeuroFuzzy Inference System (ANFIS) with Gaussian and Generalized Bell membership functions. The insample period 19502006 is examined and the forecasting performance of the two approaches is evaluated during the outof sample period 20072010. The estimation results show that the ANFIS model outperforms the Logit and Probit model. This indicates that neurofuzzy model provides a better and more reliable signal on whether or not a financial crisis will take place.
Item Type:  MPRA Paper 

Original Title:  Study of Discrete Choice Models and Adaptive NeuroFuzzy Inference System in the Prediction of Economic Crisis Periods in USA 
Language:  English 
Keywords:  Discrete choice models, NeuroFuzzy, Fuzzy rules, Membership functions, Financial crisis, US economy 
Subjects:  C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C25  Discrete Regression and Qualitative Choice Models ; Discrete Regressors ; Proportions ; Probabilities C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics > C45  Neural Networks and Related Topics 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 G  Financial Economics > G0  General > G01  Financial Crises 
Item ID:  71218 
Depositing User:  Eleftherios Giovanis 
Date Deposited:  13 May 2016 04:40 
Last Modified:  27 Sep 2019 10:03 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/71218 