On Forecasting Recessions via Neural Nets
AbstractIn this research, we employ artificial neural networks in conjunction with selected economic and financial variables to forecast recessions in Canada, France, Germany, Italy, Japan, UK, and USA. We model the relationship between selected economic and financial (indicator) variables and recessions 1-10 periods in future out-of-sample recursively. The out-of-sample forecasts from neural network models show that among the 10 models constructed from 7 indicator variables and their combinations that we investigate, the stock price index (index) and spread between bank rates and risk free rates (BRTB) are most likely candidate variables for possible forecasts of recessions 1-10 periods ahead for most countries.
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Bibliographic InfoArticle provided by AccessEcon in its journal Economics Bulletin.
Volume (Year): 3 (2008)
Issue (Month): 13 ()
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business cycles neural network out-of-sample forecasts recession real GDP;
Find related papers by JEL classification:
- C0 - Mathematical and Quantitative Methods - - General
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