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Recognizing Business Cycle Turning Points by Means of a Neural Network

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  • Vishwakarma, Keshav P

Abstract

The 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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  • Vishwakarma, Keshav P, 1994. "Recognizing Business Cycle Turning Points by Means of a Neural Network," Computational Economics, Springer;Society for Computational Economics, vol. 7(3), pages 175-185.
    • Handle: RePEc:kap:compec:v:7:y:1994:i:3:p:175-85
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    Cited by:

    1. 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.
    2. Khurshid Kiani, 2005. "Detecting Business Cycle Asymmetries Using Artificial Neural Networks and Time Series Models," Computational Economics, Springer;Society for Computational Economics, vol. 26(1), pages 65-89, August.

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