Forecasting Gross Domestic Product per Capita Using Artificial Neural Networks with Non-Economical Parameters

Gross Domestic Product per capita is one of the most important indicators of social welfare. All countries try to increase their Gross Domestic Product per capita to contribute to their population's happiness and well-being, as well as strengthen their nation's standing in international relations. Economic growth is affected by economic parameters such as trade, import, and export. However, Gross Domestic Product may also be affected by non-economic factors. Therefore, for a country to increase its Gross Domestic Product per capita, it is important to employ the correct strategy. The aim of this study is to investigate the predictability of Gross Domestic Product per capita based on non-economic data by using artificial neural network with feed forward back-propagation learning algorithm. For this purpose, neural network models have been developed with different architectures. Education level, number of published academic paper per capita, number of researchers per employed, percentage of Research and Development expenditure in the Gross Domestic Product and number of patents per capita are used as input data in the models. The input data has been collected from variety of resources such as Organisation for Economic Cooperation and Development. A comparison between the model results and actual data give a high correlation coefficient (R-2 = 0.96) and show that the model is able to predict the Gross Domestic Product per capita from non-economic parameters. (C) 2018 Elsevier B.V. All rights reserved.