Hamid Abrishami - Tehran
Hojatallah Ghanimi Fard - the Petroleum University of Technology, Tehran
Mehdi Ahrari - Tehran
Zahra Rahimi - Tehran

        This paper proposes a new forecasting model for investigating relationship between the price of crude oil, as an important energy source and GDP of the US, as the largest oil consumer, and the UK, as the oil producer. GMDH neural network and MLFF neural network approaches, which are both non-linear models, are employed to forecast GDP responses to the oil price changes. The results are compared with the results obtained by the ARIMA linear model. Using the annual data of these countries from 1952 to 2010, the empirical results indicate that the GMDH neural network using lagged GDP and oil prices yields the least error in forecasting for the US and the UK.       JEL Classification: C18, Q47   

Crude Oil Price, Crude Oil Price, GDP, GMDH &, GDP, MLFF N eural Network, ARIMA, GMDH & MLFF N eural Network, ARIMA