DESIGNING A FORECAST MODEL FOR ECONOMIC GROWTH OF JAPAN USING COMPETITIVE (HYBRID ANN VS MULTIPLE REGRESSION) MODELS

Authors

  • Ahmet DEMIR Ishik University, Erbil, Iraq
  • Atabek SHADMANOV Ishik University, Erbil
  • Cumhur AYDINLI Ipek University, Ankara
  • Okan ERAY International Black Sea University, Tbilisi

Keywords:

Artificial Neural Network, Hybrid Model, GDP Growth of Japan, Modelling Forecast, Variable Determination

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.

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Published

20.07.2015

Issue

Vol. 4 No. 2 (2015)

Section

Accounting, Finance, Statistics and Economic informatics

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