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Forecasting GDP Growth Using Artificial Neural Networks

Author

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  • Tkacz, Greg
  • Hu, Sarah

Abstract

Financial and monetary variables have long been known to contain useful leading information regarding economic activity. 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.

Suggested Citation

  • Tkacz, Greg & Hu, Sarah, 1999. "Forecasting GDP Growth Using Artificial Neural Networks," Staff Working Papers 99-3, Bank of Canada.
  • Handle: RePEc:bca:bocawp:99-3
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    References listed on IDEAS

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    5. Chung-Ming Kuan, 2006. "Artificial Neural Networks," IEAS Working Paper : academic research 06-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    6. Donald P. Morgan, 1993. "Asymmetric effects of monetary policy," Economic Review, Federal Reserve Bank of Kansas City, issue Q II, pages 21-33.
    7. Rhee, Wooheon & Rich, Robert W., 1995. "Inflation and the asymmetric effects of money on output fluctuations," Journal of Macroeconomics, Elsevier, vol. 17(4), pages 683-702.
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    Citations

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    Cited by:

    1. José Luis Torres, 2006. "Modelos para la Inflación Básica de Bienes Transables y No Transables en Colombia," BORRADORES DE ECONOMIA 003246, BANCO DE LA REPÚBLICA.
    2. Haider, Adnan & Hanif, Muhammad Nadeem, 2007. "Inflation Forecasting in Pakistan using Artificial Neural Networks," MPRA Paper 14645, University Library of Munich, Germany.
    3. Martha Misas Arango & Enrique López Enciso & Pablo Querubín, 2002. "La Inflación En Colombia: Una Aproximación Desde Las Redes Neuronales," ENSAYOS SOBRE POLÍTICA ECONÓMICA, BANCO DE LA REPÚBLICA - ESPE, vol. 20(41-42), pages 143-214, June.
    4. Christian A. Johnson, 2005. "Modelos de alerta temprana para pronosticar crisis bancarias: desde la extracción de señales a las redes neuronales," Revista de Analisis Economico – Economic Analysis Review, Ilades-Georgetown University, Universidad Alberto Hurtado/School of Economics and Bussines, vol. 20(1), pages 95-121, June.
    5. Christian A. Johnson & Rodrigo Vergara, 2005. "The implementation of monetary policy in an emerging economy: the case of Chile," Revista de Analisis Economico – Economic Analysis Review, Ilades-Georgetown University, Universidad Alberto Hurtado/School of Economics and Bussines, vol. 20(1), pages 45-62, June.
    6. María Clara Aristizábal Restrepo, 2006. "Evaluación asimétrica de una red neuronal artificial:Aplicación al caso de la inflación en Colombia," Borradores de Economia 377, Banco de la Republica de Colombia.
    7. Basihos, Seda, 2016. "Nightlights as a Development Indicator: The Estimation of Gross Provincial Product (GPP) in Turkey," MPRA Paper 75553, University Library of Munich, Germany, revised 09 Sep 2016.

    More about this item

    Keywords

    Econometric and statistical Methods; Monetary and financial indicators;

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy

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