Neural network methods for forecasting turning points in economic time series: an asymmetric verification to business cycles

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	Neural network methods for forecasting turning points in economic time series: an asymmetric verification to business cycles
	Zhang Dabin 1; Yu Lean 2; Wang Shouyang2 ; Xie Haibin 2
	2010
发表期刊	Frontiers of Computer Science in China
ISSN	1673-7350
卷号	4 期号:2 页码:254
摘要	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-ofsample 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
语种	英语
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/11000
专题	系统科学研究所
作者单位	1.Department of Information Management, Huazhong Normal University 2.中国科学院数学与系统科学研究院
推荐引用方式 GB/T 7714	Zhang Dabin,Yu Lean,Wang Shouyang,et al. Neural network methods for forecasting turning points in economic time series: an asymmetric verification to business cycles[J]. Frontiers of Computer Science in China,2010,4(2):254.
APA	Zhang Dabin,Yu Lean,Wang Shouyang,&Xie Haibin.(2010).Neural network methods for forecasting turning points in economic time series: an asymmetric verification to business cycles.Frontiers of Computer Science in China,4(2),254.
MLA	Zhang Dabin,et al."Neural network methods for forecasting turning points in economic time series: an asymmetric verification to business cycles".Frontiers of Computer Science in China 4.2(2010):254.