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Forecasting GDP Growth Using Artificial Neural Networks

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Author Info

  • 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.

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File URL: http://www.bankofcanada.ca/wp-content/uploads/2010/05/wp99-3.pdf
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Bibliographic Info

Paper provided by Bank of Canada in its series Working Papers with number 99-3.

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Length: 33 pages
Date of creation: 1999
Date of revision:
Handle: RePEc:bca:bocawp:99-3

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Keywords: Econometric and statistical Methods; Monetary and financial indicators;

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References

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  1. Swanson, N.R. & White, H., 1995. "A Models Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks," Papers 04-95-12, Pennsylvania State - Department of Economics.
  2. Perron, Pierre, 1989. "The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis," Econometrica, Econometric Society, vol. 57(6), pages 1361-1401, November.
  3. Nelson, Charles R. & Plosser, Charles I., 1982. "Trends and random walks in macroeconmic time series : Some evidence and implications," Journal of Monetary Economics, Elsevier, vol. 10(2), pages 139-162.
  4. Holden,Ken & Peel,David A. & Thompson,John L., 1991. "Economic Forecasting," Cambridge Books, Cambridge University Press, number 9780521356923, November.
  5. James M. Hutchinson & Andrew W. Lo & Tomaso Poggio, 1995. "A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks," NBER Working Papers 4718, National Bureau of Economic Research, Inc.
  6. 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.
  7. Cover, James Peery, 1992. "Asymmetric Effects of Positive and Negative Money-Supply Shocks," The Quarterly Journal of Economics, MIT Press, vol. 107(4), pages 1261-82, November.
  8. Chung-Ming Kuan, 2006. "Artificial Neural Networks," IEAS Working Paper : academic research 06-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.
  9. Donald P. Morgan, 1993. "Asymmetric effects of monetary policy," Economic Review, Federal Reserve Bank of Kansas City, issue Q II, pages 21-33.
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Citations

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Cited by:
  1. María Clara Aristizábal Restrepo, . "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.
  2. Martha Misas Arango & Enrique López Enciso & Pablo Querubín Borrero, 2002. "La Inflación en Colombia: Una Aproximación desde las Redes Neuronales," BORRADORES DE ECONOMIA 003029, BANCO DE LA REPÚBLICA.
  3. Haider, Adnan & Hanif, Muhammad Nadeem, 2007. "Inflation Forecasting in Pakistan using Artificial Neural Networks," MPRA Paper 14645, University Library of Munich, Germany.
  4. 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.
  5. 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.
  6. 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.

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