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A Hybrid Intelligent Early Warning System for Predicting Economic Crises: The Case of China


  • Su, Dongwei
  • He, Xingxing


This 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.

Suggested Citation

  • Su, Dongwei & He, Xingxing, 2010. "A Hybrid Intelligent Early Warning System for Predicting Economic Crises: The Case of China," MPRA Paper 19962, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:19962

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    References listed on IDEAS

    1. Peng, Duan & Bajona, Claustre, 2008. "China's vulnerability to currency crisis: A KLR signals approach," China Economic Review, Elsevier, vol. 19(2), pages 138-151, June.
    2. Graciela Kaminsky & Saul Lizondo & Carmen M. Reinhart, 1998. "Leading Indicators of Currency Crises," IMF Staff Papers, Palgrave Macmillan, vol. 45(1), pages 1-48, March.
    3. Kumar, Mohan & Moorthy, Uma & Perraudin, William, 2003. "Predicting emerging market currency crashes," Journal of Empirical Finance, Elsevier, vol. 10(4), pages 427-454, September.
    4. Morris Goldstein & Carmen M. Reinhart, 2000. "Assessing Financial Vulnerability: An Early Warning System for Emerging Markets," Peterson Institute Press: All Books, Peterson Institute for International Economics, number 100.
    5. Carmen M. Reinhart & Graciela L. Kaminsky, 1999. "The Twin Crises: The Causes of Banking and Balance-of-Payments Problems," American Economic Review, American Economic Association, vol. 89(3), pages 473-500, June.
    6. Beckmann, Daniela & Menkhoff, Lukas & Sawischlewski, Katja, 2006. "Robust lessons about practical early warning systems," Journal of Policy Modeling, Elsevier, vol. 28(2), pages 163-193, February.
    7. Reinhart, Carmen & Goldstein, Morris & Kaminsky, Graciela, 2000. "Assessing financial vulnerability, an early warning system for emerging markets: Introduction," MPRA Paper 13629, University Library of Munich, Germany.
    8. Niemira, Michael P. & Saaty, Thomas L., 2004. "An Analytic Network Process model for financial-crisis forecasting," International Journal of Forecasting, Elsevier, vol. 20(4), pages 573-587.
    9. Bussiere, Matthieu & Fratzscher, Marcel, 2006. "Towards a new early warning system of financial crises," Journal of International Money and Finance, Elsevier, vol. 25(6), pages 953-973, October.
    10. Alvarez-Plata, Patricia & Schrooten, Mechthild, 2004. "Misleading indicators? The Argentinean currency crisis," Journal of Policy Modeling, Elsevier, vol. 26(5), pages 587-603, July.
    11. Frankel, Jeffrey A. & Rose, Andrew K., 1996. "Currency crashes in emerging markets: An empirical treatment," Journal of International Economics, Elsevier, vol. 41(3-4), pages 351-366, November.
    12. KOMULAINEN Tuomas LUKKARILA Johanna, "undated". "What Drives Financial Crises in Emerging Markets?," EcoMod2003 330700082, EcoMod.
    13. Berg, Andrew & Pattillo, Catherine, 1999. "Predicting currency crises:: The indicators approach and an alternative," Journal of International Money and Finance, Elsevier, vol. 18(4), pages 561-586, August.
    14. repec:wsi:wschap:9789814749589_0011 is not listed on IDEAS
    15. Kalotychou, Elena & Staikouras, Sotiris K., 2006. "An empirical investigation of the loan concentration risk in Latin America," Journal of Multinational Financial Management, Elsevier, vol. 16(4), pages 363-384, October.
    16. Komulainen, Tuomas & Lukkarila, Johanna, 2003. "What drives financial crises in emerging markets?," BOFIT Discussion Papers 5/2003, Bank of Finland, Institute for Economies in Transition.
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    More about this item


    Computational intelligence; artificial neural networks; fuzzy optimization; early warning system; economic crises;

    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

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