An Analysis of the Relationship of Imports and Economic Growth in Iran (Comparison of Systematic and Unsystematic Cointegration Methods with Neural Network)

Abstract

The present study is intended to analyse the relationship of imports and economic growth in Iran using systematic and unsystematic cointegration methods and neural networks and to compare them with each other. The data used in this study are the real gross domestic product (GDP) and the total imports of Islamic Republic of Iran during the years 1961 to 2010. In this study, the concerned time series were tested by unit root testing. Then the data were examined and the results were analysed using an autoregressive distributed lag modelling (ARDL), error correction model (ECM), and maximum likelihood method of Johansen-Julius. The statistical and estimated processes of the present study were carried out using Microfit and EViews 7 software.The artificial neural networks were also modelled by MATLAB software. The findings show that no cointegration relationship is supported between GDP and imports when the real GDP is a dependent variable and total import is an independent variable. However, the existence of cointegration relationship between total import and real GDP is supported when the total import is a dependent variable and the GDP is an independent variable. The use of neural network for modelling of the relationship of two variables shows a reliable result.

Keywords

• Economic Growth, Total Import, Autoregressive Distributed Lab Modelling (ARDL), Error Correction Model (ECM), Artificial Neural Networks