Study of Discrete Choice Models and Adaptive Neuro-Fuzzy Inference System in the Prediction of Economic Crisis Periods in USA
AbstractIn 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 Neuro-Fuzzy Inference System (ANFIS) with Gaussian and Generalized Bell membership functions. The in-sample period 1950-2006 is examined and the forecasting performance of the two approaches is evaluated during the out-of sample period 2007-2010. The estimation results show that the ANFIS model outperforms the Logit and Probit model. This indicates that neuro-fuzzy model provides a better and more reliable signal on whether or not a financial crisis will take place.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Queensland University of Technology (QUT), School of Economics and Finance in its journal Economic Analysis and Policy (EAP).
Volume (Year): 42 (2012)
Issue (Month): 1 (March)
ANFIS; Discrete Choice Models; Error Back-propagation; Financial Crisis; Fuzzy Logic; US Economy;
Find related papers by JEL classification:
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- 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
- G01 - Financial Economics - - General - - - Financial Crises
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Eleftherios Giovanis, 2010. "Application of logit model and self-organizing maps (SOMs) for the prediction of financial crisis periods in US economy," Journal of Financial Economic Policy, Emerald Group Publishing, vol. 2(2), pages 98-125, June.
- Asli DemirgÃ¼Ã§-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.
- Fioramanti, Marco, 2008.
"Predicting sovereign debt crises using artificial neural networks: A comparative approach,"
Journal of Financial Stability,
Elsevier, vol. 4(2), pages 149-164, June.
- Marco Fioramanti, 2006. "Predicting Sovereign Debt Crises Using Artificial Neural Networks: A Comparative Approach," ISAE Working Papers 72, ISTAT - Italian National Institute of Statistics - (Rome, ITALY).
- Reuven Glick & Ramon Moreno, 1999.
"Money and credit, competitiveness, and currency crises in Asia and Latin America,"
Pacific Basin Working Paper Series
99-01, Federal Reserve Bank of San Francisco.
- Glick, R. & Moreno, R., 1999. "Money and Credit, Competitiveness, and Currency Crises in Asia and Latin America," Papers 99-01, Economisch Institut voor het Midden en Kleinbedrijf-.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Manuela Torgler).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.