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

Jen-Yu Lee, Tien-Thinh Nguyen, Hong-Giang Nguyen and Jen-Yao Lee
Additional contact information
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
Jen-Yao Lee: Department of International Business, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan

Energies, 2022, vol. 15, issue 11, 1-15

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.

Keywords: crude oil purchase price; forecasting; ARIMA model; SARIMA model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/11/4003/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/11/4003/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:11:p:4003-:d:827287

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().