Macroeconomic forecasting: a comparison between artificial neural networks and econometric models.

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dc.contributor.author Kabundi, Alain Ntumba
dc.date.accessioned 2008-06-17T13:52:58Z
dc.date.available 2008-06-17T13:52:58Z
dc.date.issued 2008-06-17T13:52:58Z
dc.identifier.uri http://hdl.handle.net/10210/633
dc.description.abstract In this study the prediction capabilities of Artificial Neural Networks and typical econometric methods are compared. This is done in the domains of Finance and Economics. Initially, the Neural Networks are shown to outperform traditional econometric models in forecasting nonlinear behaviour. The comparison is extended to indicate that the accuracy of share price forecasting is not necessarily improved when applying Neural Networks rather than traditional time series analysis. Finally, Neural Networks are used to forecast the South African inflation rates, and its performance is compared to that of vector error correcting models, which apparently outperform Artificial Neural Networks. en
dc.description.sponsorship Prof. D.J. Marais en
dc.language.iso en en
dc.subject neural networks en
dc.subject macroeconomics en
dc.subject economic forecasting en
dc.subject econometric models en
dc.title Macroeconomic forecasting: a comparison between artificial neural networks and econometric models. en
dc.type Thesis en

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