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Detecting Business Cycle Asymmetries Using Artificial Neural Networks and Time Series Models

  • Khurshid Kiani

This study examines possible existence of business cycle asymmetries in Canada, France, Japan, UK, and USA real GDP growth rates using neural networks nonlinearity tests and tests based on a number of nonlinear time series models. These tests are constructed using in-sample forecasts from artificial neural networks (ANN) as well as time series models. Our study results based on neural network tests show that there is statistically significant evidence of business cycle asymmetries in these industrialized countries. Similarly, our study results based on a number of time series models also show that business cycle asymmetries do prevail in these countries. So we are not able to evaluate the impact of monetary policy or any other shocks on GDP in these countries based on linear models. Copyright Springer Science + Business Media, Inc. 2005

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File URL: http://hdl.handle.net/10.1007/s10614-005-7366-2
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Article provided by Society for Computational Economics in its journal Computational Economics.

Volume (Year): 26 (2005)
Issue (Month): 1 (August)
Pages: 65-89

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Handle: RePEc:kap:compec:v:26:y:2005:i:1:p:65-89
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