The results indicate the best model is SARFIMA (0, 1, 1) (0, -0.199, 0)12 which is used to predict the quantity of oil supply in Iran till the end of 2020.
📌 3 Key Takeaways
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Objective & Hypothesis:In this paper the specification of long memory has been studied using monthly data in total oil supply in Iran from 1994 to 2009.
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Key Findings & Data:The results indicate the best model is SARFIMA (0, 1, 1) (0, -0.199, 0)12 which is used to predict the quantity of oil supply in Iran till the end of 2020.
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Conclusion & Impact:Therefore SARFIMA model can be used as the best model for predicting the amount of oil supply in the future.
🔬Design:Modeling & Simulation
📍Scope:2009 Verileri
⚙️Variables:Long memory · Conditional sum of squares · SARFIMA model
Abstract
In this paper the specification of long memory has been studied using monthly data in total oil supply in Iran from 1994 to 2009. Because monthly oil supply series in Iran are showing non-stationary and periodic behavior we fit the data with SARIMA and SARFIMA models, and estimate the parameters using conditional sum of squares method. The results indicate the best model is SARFIMA (0, 1, 1) (0, -0.199, 0)12 which is used to predict the quantity of oil supply in Iran till the end of 2020. Therefore SARFIMA model can be used as the best model for predicting the amount of oil supply in the future.
Mostafaei, H., SAKHABAKHSH, L. (2012). Using SARFIMA Model to Study and Predict the Iran’s Oil Supply. International Journal of Energy Economics and Policy, Cilt: 2 Sayı: 1, 41–49. https://ajindex.com/articles/995695