Recognizing Business Cycle Turning Points by Means of a Neural Network
AbstractThe latest, 1990-91 recession marks the ninth downturn in the U.S. economy during the past fifty years. There is scope for adding extensions to the methodology of monitoring such major economic fluctuations. The use of artificial neural networks is proposed here. For demonstration a case study is included. In it four key economic indicators are examined; viz., sales, production, employment and personal income. The growth rate movement common to these variables is represented by a state space model of dynamic systems theory. Their monthly time series data over 1965-1989 are simultaneously analyzed. The dates of business cycle peaks and troughs identified in the analysis agree closely with the official chronology. Citation Copyright 1994 by Kluwer Academic Publishers.
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Bibliographic InfoArticle provided by Society for Computational Economics in its journal Computational Economics.
Volume (Year): 7 (1994)
Issue (Month): 3 ()
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- 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.
- Qi, Min, 2001. "Predicting US recessions with leading indicators via neural network models," International Journal of Forecasting, Elsevier, vol. 17(3), pages 383-401.
- Bildirici, Melike & Alp, Aykaç, 2008. "The Relationship Between Wages and Productivity: TAR Unit Root and TAR Cointegration Approach," International Journal of Applied Econometrics and Quantitative Studies, Euro-American Association of Economic Development, vol. 5(1), pages 93-110.
- Kiani, K.M., 2009. "Neural Networks to Detect Nonlinearities in Time Series: Analysis of Business Cycle in France and the United Kingdom," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 9(1).
- Khurshid M. Kiani, 2009. "Asymmetries in Macroeconomic Time Series in Eleven Asian Economies," International Journal of Business and Economics, College of Business, and College of Finance, Feng Chia University, Taichung, Taiwan, vol. 8(1), pages 37-54, April.
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