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DESIGNING A FORECAST MODEL FOR ECONOMIC GROWTH OF JAPAN USING COMPETITIVE (HYBRID ANN VS MULTIPLE REGRESSION) MODELS Abstract : Artificial neural network models have been already used on many different fields successfully. However, many researches show that ANN models provide better optimum results than other competitive models in most of the researches. But does it provide optimum solutions in case ANN is proposed as hybrid model? The answer of this question is given in this research by using these models on modelling a forecast for GDP growth of Japan. Multiple regression models utilized as competitive models versus hybrid ANN (ANN + multiple regression models). Results have shown that hybrid model gives better responds than multiple regression models. However, variables, which were significantly affecting GDP growth, were determined and some of the variables, which were assumed to be affecting GDP growth of Japan,were eliminated statistically

Author

Listed:
  • Ahmet DEMIR

    (Ishik University, Erbil, Iraq)

  • AtabekSHADMANOV

    (Ishik University, Erbil, Iraq)

  • CumhurAYDINLI

    (Ipek University, Ankara, Turkey,)

  • Okan ERAY

    (International Black Sea University, Tbilisi, Georgia,)

Abstract

No abstract is available for this item.

Suggested Citation

  • Ahmet DEMIR & AtabekSHADMANOV & CumhurAYDINLI & Okan ERAY, 2015. "DESIGNING A FORECAST MODEL FOR ECONOMIC GROWTH OF JAPAN USING COMPETITIVE (HYBRID ANN VS MULTIPLE REGRESSION) MODELS Abstract : Artificial neural network models have been already used on many differen," EcoForum, "Stefan cel Mare" University of Suceava, Romania, Faculty of Economics and Public Administration - Economy, Business Administration and Tourism Department., vol. 4(2), pages 1-21, july.
    • Handle: RePEc:scm:ecofrm:v:4:y:2015:i:2:p:21
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    References listed on IDEAS

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    3. Greg Tkacz & Sarah Hu, 1999. "Forecasting GDP Growth Using Artificial Neural Networks," Staff Working Papers 99-3, Bank of Canada.
    4. Cargill, Thomas F. & Parker, Elliott, 2004. "Price deflation and consumption: central bank policy and Japan's economic and financial stagnation," Journal of Asian Economics, Elsevier, vol. 15(3), pages 493-506, June.
    5. Norman R. Swanson & Halbert White, 1997. "A Model Selection Approach To Real-Time Macroeconomic Forecasting Using Linear Models And Artificial Neural Networks," The Review of Economics and Statistics, MIT Press, vol. 79(4), pages 540-550, November.
    6. Kaihatsu, Sohei & Kurozumi, Takushi, 2014. "What caused Japan’s Great Stagnation in the 1990s? Evidence from an estimated DSGE model," Journal of the Japanese and International Economies, Elsevier, vol. 34(C), pages 217-235.
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