A Hybrid Intelligent Early Warning System for Predicting Economic Crises: The Case of China
AbstractThis paper combines artificial neural networks (ANN), fuzzy optimization and time-series econometric models in one unified framework to form a hybrid intelligent early warning system (EWS) for predicting economic crises. Using quarterly data on 12 macroeconomic and financial variables for the Chinese economy during 1999 and 2008, the paper finds that the hybrid model possesses strong predictive power and the likelihood of economic crises in China during 2009 and 2010 remains high.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 19962.
Date of creation: 11 Jan 2010
Date of revision:
Computational intelligence; artificial neural networks; fuzzy optimization; early warning system; economic crises;
Find related papers by JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-01-23 (All new papers)
- NEP-CMP-2010-01-23 (Computational Economics)
- NEP-FOR-2010-01-23 (Forecasting)
- NEP-TRA-2010-01-23 (Transition Economics)
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