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

Author

Listed:
  • Hamidreza Mostafaei

    (Department of Statistics, North Tehran Branch,Islamic Azad University, Tehran, Iran)

  • Leila Sakhabakhsh

    (Department of Statistics, North Tehran Branch,Islamic Azad University, Tehran, Iran)

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 nonstationary 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.

Suggested Citation

  • Hamidreza Mostafaei & Leila Sakhabakhsh, 2012. "Using SARFIMA Model to Study and Predict the Iran s Oil Supply," International Journal of Energy Economics and Policy, Econjournals, vol. 2(1), pages 41-49.
    • Handle: RePEc:eco:journ2:2012-01-5
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    References listed on IDEAS

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    Cited by:

    1. Shaher Al-Gounmeein Remal & Ismail Mohd Tahir, 2021. "Modelling and forecasting monthly Brent crude oil prices: a long memory and volatility approach," Statistics in Transition New Series, Statistics Poland, vol. 22(1), pages 29-54, March.

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    More about this item

    Keywords

    Long memory; Conditional sum of squares; SARFIMA model; Oil; Iran;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

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