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Complex method of petroleum products demand forecasting considering economic, demographic and technological factors

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  • Ekaterina Grushevenko

Abstract

The article describes study results on the development of methods for forecasting the demand for petroleum products, some petroleum product groups in the world and in some countries node(nodes). The developed method is based on the combination of different forecasting approaches: correlation, factor, technical, economic and econometric analysis with mathematic economic model elements. The method may be used in the performance of a wide range of tasks: from specific petroleum product market forecasting, such as aviation kerosene or motor gasoline, to system study of the world petroleum market development prospects and the future petroleum role in the energy balance of individual countries and regions.Â

Suggested Citation

  • Ekaterina Grushevenko, 2015. "Complex method of petroleum products demand forecasting considering economic, demographic and technological factors," Economics and Business Letters, Oviedo University Press, vol. 4(3), pages 98-107.
    • Handle: RePEc:ove:journl:aid:10755
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    File URL: https://reunido.uniovi.es/index.php/EBL/article/view/10755
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    1. Dmitriy Grushevenko & Ekaterina Grushevenko & Vyacheslav Kulagin, 2018. "Energy Consumption of the Russian Road Transportation Sector: Prospects for Inter-Fuel Competition in Terms of Technological Innovation," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 12(4), pages 35-44.
    2. N. O. Kapustin & D. A. Grushevenko, 2023. "Assessment of Long-Term Prospects for Demand in the Plastics Market in the Face of Industry Transformation," Studies on Russian Economic Development, Springer, vol. 34(2), pages 243-253, April.
    3. Baratsas, Stefanos G. & Niziolek, Alexander M. & Onel, Onur & Matthews, Logan R. & Floudas, Christodoulos A. & Hallermann, Detlef R. & Sorescu, Sorin M. & Pistikopoulos, Efstratios N., 2022. "A novel quantitative forecasting framework in energy with applications in designing energy-intelligent tax policies," Applied Energy, Elsevier, vol. 305(C).

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