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On Forecasting Recessions via Neural Nets

  • Yasuhiko Nakamura


    (Graduate School of Economics, Waseda University)

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    In this research, we employ artificial neural networks in conjunction with selected economic and financial variables to forecast recessions in Canada, France, Germany, Italy, Japan, UK, and USA. We model the relationship between selected economic and financial (indicator) variables and recessions 1-10 periods in future out-of-sample recursively. The out-of-sample forecasts from neural network models show that among the 10 models constructed from 7 indicator variables and their combinations that we investigate, the stock price index (index) and spread between bank rates and risk free rates (BRTB) are most likely candidate variables for possible forecasts of recessions 1-10 periods ahead for most countries.

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    Article provided by AccessEcon in its journal Economics Bulletin.

    Volume (Year): 3 (2008)
    Issue (Month): 13 ()
    Pages: 1-15

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    Handle: RePEc:ebl:ecbull:eb-06c00010
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    1. Allan D. Brunner, 1997. "On The Dynamic Properties Of Asymmetric Models Of Real GNP," The Review of Economics and Statistics, MIT Press, vol. 79(2), pages 321-352, May.
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    8. Hutchinson, James M & Lo, Andrew W & Poggio, Tomaso, 1994. " A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks," Journal of Finance, American Finance Association, vol. 49(3), pages 851-89, July.
    9. Khurshid M. Kiani & Prasad V. Bidarkota, 2004. "On Business Cycle Asymmetries in G7 Countries," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(3), pages 333-351, 07.
    10. Prasad V. Bidarkota, 2000. "Asymmetries in the Conditional Mean Dynamics of Real GNP: Robust Evidence," The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 153-157, February.
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