Using SARFIMA Model to Study and Predict the Iran's Oil Supply

Authors

  • Hamidreza Mostafaei
  • Leila Sakhabakhsh

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.Keywords: Long memory; Conditional sum of squares; SARFIMA model; Oil; IranJEL Classifications: C12; C13; C22; C50

Downloads

Download data is not yet available.

Downloads

Published

2011-12-05

How to Cite

Mostafaei, H., & Sakhabakhsh, L. (2011). Using SARFIMA Model to Study and Predict the Iran’s Oil Supply. International Journal of Energy Economics and Policy, 2(1), 41–49. Retrieved from https://econjournals.com/index.php/ijeep/article/view/107

Issue

Vol. 2 No. 1 (2012)

Section

Articles

License

Submission of an article implies that the work described has not been published previously , that it is not under consideration for publication elsewhere, that its publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out, and that, if accepted, will not be published elsewhere in the same form, without the written consent of the Publisher. The Editors reserve the right to edit or otherwise alter all contributions, but authors will receive proofs for approval before publication.