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Forecasting GDP growth using artificial neural networks / by Greg Tkacz and Sarah Hu. : FB3-2/99-3E

In this paper, the authors wish to determine whether the forecasting performance of such variables can be improved using neural network models. The main findings are that, at the 1-quarter forecasting horizon, neural networks yield no significant forecast improvements. At the 4-quarter horizon, however, the improved forecast accuracy is statistically significant. The root mean squared forecast errors of the best neural network models are about 15 to 19 per cent lower than their linear model counterparts. The improved forecast accuracy may be capturing more fundamental non-linearities between financial variables and real output growth at the longer horizon.--Abstract

Lien permanent pour cette publication :
publications.gc.ca/pub?id=9.614702&sl=1

Renseignements sur la publication
Ministère/Organisme
  • Bank of Canada.
TitreForecasting GDP growth using artificial neural networks / by Greg Tkacz and Sarah Hu.
Titre de la série
  • Working paper 1192-5434 99-3
Type de publicationMonographie - Voir l'enregistrement principal
Langue[Anglais]
FormatTexte matériel
Autres formats offertsTexte numérique-[Anglais]
Note(s)
  • "In this paper, the authors wish to determine whether the forecasting performance of such variables can be improved using neural network models. The main findings are that, at the 1-quarter forecasting horizon, neural networks yield no significant forecast improvements. At the 4-quarter horizon, however, the improved forecast accuracy is statistically significant. The root mean squared forecast errors of the best neural network models are about 15 to 19 per cent lower than their linear model counterparts. The improved forecast accuracy may be capturing more fundamental non-linearities between financial variables and real output growth at the longer horizon."--Abstract.
  • Résumés en français
Information sur la publication
  • Ottawa - Ontario : Bank of Canada 1999.
ReliureSoftcover
Description24p. : graphs, references, tables ; 28 cm.
ISBN0-662-27537-3
ISSN1192-5434
Numéro de catalogue
  • FB3-2/99-3E
Numéro de catalogue du ministère99-3
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