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

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 Bank of Canada working paper1701-939799-3
Publication type Series - View Master Record
Language [English]
Format Electronic
Electronic document
Other formats Paper-[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.
The ISBN (0-662-27537-3) and ISSN (1192-5434) for the print edition have been incorrectly copied in this electronic publication.
Résumé en français.
Publishing information Ottawa - Ontario : Bank of Canada January 1999.
Description 33p.figs., graphs, references, tables
ISSN 1701-9397
Catalogue number
  • FB3-2/99-3E-PDF
Subject terms Gross national product
Forecasting
Models
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