On Forecasting Recessions via Neural Nets
AbstractIn 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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Bibliographic InfoArticle provided by AccessEcon in its journal Economics Bulletin.
Volume (Year): 3 (2008)
Issue (Month): 13 ()
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business cycles neural network out-of-sample forecasts recession real GDP;
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- C0 - Mathematical and Quantitative Methods - - General
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- Beaudry, Paul & Koop, Gary, 1993. "Do recessions permanently change output?," Journal of Monetary Economics, Elsevier, vol. 31(2), pages 149-163, April.
- Prasad Bidarkota & Khurshid M. Kiani, 2003.
"On Business Cycle Asymmetries in G7 Countries,"
0308, Florida International University, Department of Economics.
- Neftci, Salih N, 1984. "Are Economic Time Series Asymmetric over the Business Cycle?," Journal of Political Economy, University of Chicago Press, vol. 92(2), pages 307-28, April.
- Allan D. Brunner, 1994.
"On the dynamic properties of asymmetric models of real GNP,"
International Finance Discussion Papers
489, Board of Governors of the Federal Reserve System (U.S.).
- 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.
- James M. Hutchinson & Andrew W. Lo & Tomaso Poggio, 1995.
"A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks,"
NBER Working Papers
4718, National Bureau of Economic Research, Inc.
- 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.
- Khurshid Kiani, 2005. "Detecting Business Cycle Asymmetries Using Artificial Neural Networks and Time Series Models," Computational Economics, Society for Computational Economics, vol. 26(1), pages 65-89, August.
- 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.
- René Garcia & Ramazan Gençay, 1998.
"Pricing and Hedging Derivative Securities with Neural Networks and a Homogeneity Hint,"
CIRANO Working Papers
- Garcia, Rene & Gencay, Ramazan, 2000. "Pricing and hedging derivative securities with neural networks and a homogeneity hint," Journal of Econometrics, Elsevier, vol. 94(1-2), pages 93-115.
- Dorsey, Robert E & Mayer, Walter J, 1995.
"Genetic Algorithms for Estimation Problems with Multiple Optima, Nondifferentiability, and Other Irregular Features,"
Journal of Business & Economic Statistics,
American Statistical Association, vol. 13(1), pages 53-66, January.
- Michael B. Gordy, . "GA.M: A Matlab routine for function maximization using a Genetic Algorithm," Matlab codes ga, , revised 12 Feb 1996.
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