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A Comparison Between Time Series, Exponential Smoothing, and Neural Network Methods To Forecast GDPof Iran

Author

Listed:
  • Ahmad Jafari-Samimi

    (Professor of Economics at The University of Mazandaran, Babolsar, Iran)

  • Babak Shirazi

    (Ph.D students at The Sciences and Technologies University of Mazandaran, Babolsar, Iran)

  • Hamed Fazlollahtabar

    (Ph.D students at The Sciences and Technologies University of Mazandaran, Babolsar, Iran)

Abstract

In general gross domestic product (GDP) is a substantial element in macro-economic analysis. Policy makers of a country use variations of GDP for long run planning. Considering different economic conditions of a country, forecasting is a useful tool to identify the variations of GDP for planning. In this paper, quarterly GDP value during (1998-2003) is used as a base of analysis. The quarterly GDP values of the year (2004 -2005) are forecasted using Time series, Exponential smoothing and Neural network approaches. The results are compared with actual quarterly GDP value and error measurement are computed in each methods. Consequently statistical analyses are accomplished to show the best method of forecasting. We have shown that neural network approach method is the best alternative to forecast the GDP of Iran.

Suggested Citation

  • Ahmad Jafari-Samimi & Babak Shirazi & Hamed Fazlollahtabar, 2007. "A Comparison Between Time Series, Exponential Smoothing, and Neural Network Methods To Forecast GDPof Iran," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 12(2), pages 19-35, spring.
    • Handle: RePEc:eut:journl:v:12:y:2007:i:2:p:19
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    References listed on IDEAS

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    2. Koskinen, Lasse & Öller, Lars-Erik, 1998. "A Hidden Markov Model as a Dynamic Bayesian Classifier, With an Application to Forecasting Business-Cycle Turning Points," Working Papers 59, National Institute of Economic Research.
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