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

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Publication information
Department/Agency Bank of Canada.
Title Forecasting GDP growth using artificial neural networks / by Greg Tkacz and Sarah Hu.
Series title Working paper1192-543499-3
Publication type Series - View Master Record
Language [English]
Format Paper
Other formats Electronic-[English]
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
Publishing information Ottawa - Ontario : Bank of Canada 1999.
Binding Softcover
Description 24p. : graphs, references, tables ; 28 cm.
ISBN 0-662-27537-3
ISSN 1192-5434
Catalogue number
  • FB3-2/99-3E
Departmental catalogue number 99-3
Subject terms Gross national product
Forecasting
Models
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