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

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.

Keywords

turning points / business cycle / leading indicators / neural networks (NNs)

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 70971052 and 70601029), Post Doctor Foundation of China (No. 20080440539).

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg