Designing a Forecast Model for Economic Growth of Japan Using Competitive (Hybrid ANN vs Multiple Regression) ModelsAhmet Demir1*, Atabek Shadmanov1, Cumhur Aydinli2 and Okan Eray3
- *Corresponding Author:
- Ahmet Demir
Ishik University Erbil, Iraq
Tel: +964-750- 835-7525;
E-mail: [email protected]
Received date: March 17, 2015; Accepted date: April 27, 2015; Published date: May 05, 2015
Citation: Demir A, Shadmanov A, Aydinli C, Eray O (2015) Designing a Forecast Model for Economic Growth of Japan Using Competitive (Hybrid ANN vs Multiple Regression) Models. Int J Econ Manag Sci 4:254. doi:10.4172/2162-6359.1000254
Copyright: © 2015 Demir A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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 modeling 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.