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Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe

Author

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  • Jen-Yu Lee

    (Department of Statistics, Feng Chia University, Taichung City 407102, Taiwan)

  • Tien-Thinh Nguyen

    (Department of International Business, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan)

  • Hong-Giang Nguyen

    (Faculty of Architecture, Thu Dau Mot University, Thu Dau Mot 820000, Vietnam
    Department of Civil Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan)

  • Jen-Yao Lee

    (Department of International Business, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan)

Abstract

Crude oil price volatility impacts the global economy in general, as well as the economies of Europe and the United States in particular; it is supremely difficult to describe its tendency precisely, hence it leads to a forecasting methodology. This study aims to use the autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA) approaches to cope with this problem in the United States and Europe. The data was gathered from the U.S. Energy Information Administration and federal research economic data (FRED) from January 2017 to September 2021. Simultaneously, values from January 2017 to March 2021, with 51 observations accounting for 90% of the total samples, were employed for the training phase, and the rest were used for the testing phase. The forecast result also indicated that the root mean square error (RMSE) and mean absolute percentage error (MAPE) values, applied by ARIMA models in Europe and the United States, have higher accurate indicators than SARIMA models. As a result, the ARIMA model achieved the best accuracy in both Europe and the USA, with MAPE Europe − ARIMA = 0.05, and MAPE USA − ARIMA = 0.05 . Based on these accuracy parameters, the forecasting models appear incredibly reliable; similarly, the study results might assist governing bodies in making significant decisions, thereby accelerating socio-economic development in the world’s two largest economies.

Suggested Citation

  • Jen-Yu Lee & Tien-Thinh Nguyen & Hong-Giang Nguyen & Jen-Yao Lee, 2022. "Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe," Energies, MDPI, vol. 15(11), pages 1-15, May.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:4003-:d:827287
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    References listed on IDEAS

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    1. Christiane Baumeister & Lutz Kilian, 2016. "Forty Years of Oil Price Fluctuations: Why the Price of Oil May Still Surprise Us," Journal of Economic Perspectives, American Economic Association, vol. 30(1), pages 139-160, Winter.
    2. Mohammadi, Hassan & Su, Lixian, 2010. "International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models," Energy Economics, Elsevier, vol. 32(5), pages 1001-1008, September.
    3. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
    4. Mensi, Walid & Hammoudeh, Shawkat & Shahzad, Syed Jawad Hussain & Shahbaz, Muhammad, 2017. "Modeling systemic risk and dependence structure between oil and stock markets using a variational mode decomposition-based copula method," Journal of Banking & Finance, Elsevier, vol. 75(C), pages 258-279.
    5. Taiyong Li & Yingrui Zhou & Xinsheng Li & Jiang Wu & Ting He, 2019. "Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors," Energies, MDPI, vol. 12(19), pages 1-25, September.
    6. Alekhina, Victoriia & Yoshino, Naoyuki, 2018. "Impact of World Oil Prices on an Energy Exporting Economy Including Monetary Policy," ADBI Working Papers 828, Asian Development Bank Institute.
    7. Zhao, Yang & Li, Jianping & Yu, Lean, 2017. "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 9-16.
    8. Shafiee, Shahriar & Topal, Erkan, 2009. "When will fossil fuel reserves be diminished?," Energy Policy, Elsevier, vol. 37(1), pages 181-189, January.
    9. Ghoddusi, Hamed & Creamer, Germán G. & Rafizadeh, Nima, 2019. "Machine learning in energy economics and finance: A review," Energy Economics, Elsevier, vol. 81(C), pages 709-727.
    10. Wang, Yudong & Wu, Chongfeng & Yang, Li, 2014. "Oil price shocks and agricultural commodity prices," Energy Economics, Elsevier, vol. 44(C), pages 22-35.
    11. Wachtmeister, Henrik & Henke, Petter & Höök, Mikael, 2018. "Oil projections in retrospect: Revisions, accuracy and current uncertainty," Applied Energy, Elsevier, vol. 220(C), pages 138-153.
    12. Squalli, Jay, 2007. "Electricity consumption and economic growth: Bounds and causality analyses of OPEC members," Energy Economics, Elsevier, vol. 29(6), pages 1192-1205, November.
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    Cited by:

    1. Abdullah Sultan Al Shammre & Benaissa Chidmi, 2023. "Oil Price Forecasting Using FRED Data: A Comparison between Some Alternative Models," Energies, MDPI, vol. 16(11), pages 1-24, May.

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