Shahram Golestani; Majid Hatefi Majomard Majid Hatefi Majomard; Umm al-Banin Jalali
Volume 2, Issue 6 , April 2013, , Pages 151-182
Abstract
The study has calculated the price path, extraction path and discounted profit for GECF and fringe group with use of genetic algorithm on the basis of Price leadership and collusion models. In this regard, members of "Gas Exporting Countries Forum" have been considered as a pricing cartel and other producers ...
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The study has calculated the price path, extraction path and discounted profit for GECF and fringe group with use of genetic algorithm on the basis of Price leadership and collusion models. In this regard, members of "Gas Exporting Countries Forum" have been considered as a pricing cartel and other producers as the fringe group. For this purpose, annual data (1980-2010) is used for forecasting of studied trends up to year 2070.the result from price leadership model show that world gas demand Increases linearly over the time and it Increases exponentially. On this basis the supply of fringe group also grows increasingly, and the cartels supply (that is the margin between world demand and fringe supply) grows decreasingly. The results from collusion solution indicates that extraction trend is slower in compared with the price leadership solution and the price and profit in collusion solution is more than price leadership solution.
Mostafa Gorgini; Shahram Golestani; Fatemeh Hajabbasi
Volume 1, Issue 4 , October 2012, , Pages 145-168
Abstract
Awareness of the future oil demand is essential for OPEC member countries to determine priorities and policy selection for achieving economic growth and development. In this study, demand for OPEC’s oil, using time-series models Including Vector Autoregressive (VAR), and Autoregressive Integrated ...
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Awareness of the future oil demand is essential for OPEC member countries to determine priorities and policy selection for achieving economic growth and development. In this study, demand for OPEC’s oil, using time-series models Including Vector Autoregressive (VAR), and Autoregressive Integrated Moving Average (ARIMA) models and an alternative model, artificial neural network (ANN) (using monthly data from 2001:1-2010:10), is predicted. To measure the ability of predictive power of the models, three criteria are used: Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results show that VAR pattern with the error rate of 6% for the sum of squared error, mean absolute error of 19% and 5% of the average of the absolute value is the most appropriate forecast for OPEC’s oil demand. Based on VAR model, it is predicted that demand for oil is growing over all the months in the year 2012. Also, the projected demand in 2015 shows that the demand for OPEC’s oil has a rising trend but in 2014 this trend will be slower.
