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Oil Consumption Forecasting using ARIMA Models: An Empirical Study for Greece

Author

Listed:
  • Chaido Dritsaki

    (Department of Accounting and Finance, University of Western Macedonia, Kila, Kozani, Greece,)

  • Dimitrios Niklis

    (Department of Accounting and Finance, University of Western Macedonia, Kila, Kozani, Greece,)

  • Pavlos Stamatiou

    (Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece.)

Abstract

Oil is considered one of the most widely used commodity worldwide and one of the most important goods for a country s productivity. Even if the effect of renewable energy sources tries to replace the consumption of fossil fuels, such as oil, nonetheless the level of worldwide oil consumption hasn t changed. Forecasting oil consumption plays an important role on the designing of energy strategies for policy makers. This paper aims at modeling and forecasting oil consumption in Greece using Box-Jenkins methodology during 960-2020. Forecasting oil consumption was accomplished both with static and dynamic procedure, in and out-of-sample using various forecasting criteria. The results of our paper present a downturn in oil consumption for the following years due to two basic factors. The first is referred to Covid-19 pandemia where economic activity of the country decreased as well as business revenues. The second is the efforts made by the country to replace, oil consumption with other energy forms such as natural gas and mostly renewable sources like sun and wind. With these actions taken, the country member of EU is consistent with the regulations signed to Kyoto protocol where there are commitments for CO2 reduction emissions and improvement of energy use

Suggested Citation

  • Chaido Dritsaki & Dimitrios Niklis & Pavlos Stamatiou, 2021. "Oil Consumption Forecasting using ARIMA Models: An Empirical Study for Greece," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 214-224.
  • Handle: RePEc:eco:journ2:2021-04-27
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    References listed on IDEAS

    as
    1. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
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    Cited by:

    1. Pavlos Stamatiou, 2023. "Μοdeling Environmental Degradation: The Effects of Electricity Consumption, Economic Growth and Globalization," International Journal of Energy Economics and Policy, Econjournals, vol. 13(5), pages 62-72, September.
    2. Heru Wahyudi & Winda Rika Lestasi & Ratna Septiyanti & Widia Anggi Palupi, 2023. "Walking as an Alternative to Indonesia s Oil Consumption Problem," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 111-119, July.
    3. Wulfran Fendzi Mbasso & Reagan Jean Jacques Molu & Serge Raoul Dzonde Naoussi & Saatong Kenfack, 2022. "Demand-Supply Forecasting based on Deep Learning for Electricity Balance in Cameroon," International Journal of Energy Economics and Policy, Econjournals, vol. 12(4), pages 99-103, July.

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    More about this item

    Keywords

    Oil Consumption; ARIMA model; Box-Jenkins methodology; forecasting; Greece.;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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