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A Hybrid Autoregressive Integrated Moving Average-phGMDH Model to Forecast Crude Oil Price

Author

Listed:
  • Richard Sarpong-Streetor

    (Fundamental and Applied Science Department, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia)

  • Rajalingam A/L Sokkalingam

    (Fundamental and Applied Science Department, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia)

  • Mahmod bin Othman

    (Fundamental and Applied Science Department, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia)

  • Dennis Ling Chuan Ching

    (Fundamental and Applied Science Department, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia)

  • Hamzah bin Sakidin

    (Fundamental and Applied Science Department, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia)

Abstract

Crude oil price fluctuations affect almost every individual and activity on the planet. Forecasting the crude oil price is therefore an important concern especially in economic policy and financial circles as it enables stakeholders estimate crude oil price at a point in time. Autoregressive Integrated Moving Average has been an effective tool that has been used widely to model time series. Its limitation is the fact that it cannot model nonlinear systems sufficiently. This paper assesses the ability to build a robust forecasting model for the world crude oil price, Brent on the international market using a hybrid of two methods Autoregressive Integrated Moving Average and Polynomial Harmonic Group Method of Data Handling. Autoregressive Integrated Moving Average methodology is used to model the time series component with constant variance whilst the Polynomial Harmonic Group Method of Data Handling is used to model the harmonic Autoregressive Integrated Moving Average model residuals.

Suggested Citation

  • Richard Sarpong-Streetor & Rajalingam A/L Sokkalingam & Mahmod bin Othman & Dennis Ling Chuan Ching & Hamzah bin Sakidin, 2019. "A Hybrid Autoregressive Integrated Moving Average-phGMDH Model to Forecast Crude Oil Price," International Journal of Energy Economics and Policy, Econjournals, vol. 9(5), pages 135-141.
    • Handle: RePEc:eco:journ2:2019-05-16
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    References listed on IDEAS

    as
    1. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
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    More about this item

    Keywords

    Autocorrelation; Harmonics; Residuals;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products

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