Forecast performance of neural networks and business cycle asymmetries
AbstractForecast performance of artificial neural network models are investigated using Ashley et al . (1980) and the neural network nonlinearity test proposed by Lee et al . (1993) is employed to find possible existence of business cycle asymmetries in Canada, France, Japan, UK and USA real GDP growth rates. The results show that neural network models are more accurate than linear models for in-sample forecasts. However, when comparing the out-of-sample, linear models performed better than neural network models in all series. Results from neural network tests show that business cycle asymmetries do prevail in all the series.
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Bibliographic InfoArticle provided by Taylor and Francis Journals in its journal Applied Financial Economics Letters.
Volume (Year): 1 (2005)
Issue (Month): 4 (July)
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