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Neural network methods for forecasting turning points in economic time series: an asymmetric verification to business cycles |
Dabin ZHANG1,2, Lean YU2( ), Shouyang WANG2, Haibin XIE2 |
1. Department of Information Management, Huazhong Normal University, Wuhan 430079, China; 2. Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China |
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Abstract This paper examines the relevance of various financial and economic indicators in forecasting business cycle turning points using neural network (NN) models. A three-layer feed-forward neural network model is used to forecast turning points in the business cycle of China. The NN model uses 13 indicators of economic activity as inputs and produces the probability of a recession as its output. Different indicators are ranked in terms of their effectiveness of predicting recessions in China. Out-of-sample results show that some financial and economic indicators, such as steel output, M2, Pig iron yield, and the freight volume of the entire society are useful for predicting recession in China using neural networks. The asymmetry of business cycle can be verified using our NN method.
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Keywords
turning points
business cycle
leading indicators
neural networks (NNs)
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Corresponding Author(s):
YU Lean,Email:yulean@amss.ac.cn
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Issue Date: 05 June 2010
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