Asymmetric Verification of Business Cycle by Forecasting Turning Points Based on Neural Networks
This paper examines the relevance of various financial and economic indicators in forecasting business cycle turning points via neural networks (NN) models. We employ a feedforward neural network model to forecast turning points in the business cycle of China. The NN has as inputs thirteen indicators of economic activity and as output the probability of a recession. The different indicators are ranked in terms of their effectiveness of predicting China recessions. The out-of-sample results show that via the NN model indicators, such as steel output, M2, Pig iron yield and freight volume of whole society are useful in forecasting China recessions. Meanwhile, based on this method, asymmetry of business cycle can be verified.
Turning points Business cycle Leading indicators Neural network
Dabin Zhang Haibin Xie
Information Management Department Huazhong Normal University Wuhan, China, 430079 Institute of Systems Science, Academy of Mathematics and Systems Science Chinese Academy of Sciences
国际会议
黄山
英文
302-306
2010-05-28（万方平台首次上网日期，不代表论文的发表时间）