Forecasting Long-Term Crude Oil Prices Using a Bayesian Model with Informative Priors
AbstractIn the long-term, crude oil prices may impact the economic stability and sustainability of many countries, especially those depending on oil imports. This study thus suggests an alternative model for accurately forecasting oil prices while reflecting structural changes in the oil market by using a Bayesian approach. The prior information is derived from the recent and expected structure of the oil market, using a subjective approach, and then updated with available market data. The model includes as independent variables factors affecting oil prices, such as world oil demand and supply, the financial situation, upstream costs, and geopolitical events. To test the model’s forecasting performance, it is compared with other models, including a linear ordinary least squares model and a neural network model. The proposed model outperforms on the forecasting performance test even though the neural network model shows the best results on a goodness-of-fit test. The results show that the crude oil price is estimated to increase to $169.3/Bbl by 2040. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Lee, C.-Y.; Huh, S.-Y. Forecasting Long-Term Crude Oil Prices Using a Bayesian Model with Informative Priors. Sustainability 2017, 9, 190.
Lee C-Y, Huh S-Y. Forecasting Long-Term Crude Oil Prices Using a Bayesian Model with Informative Priors. Sustainability. 2017; 9(2):190.Chicago/Turabian Style
Lee, Chul-Yong; Huh, Sung-Yoon. 2017. "Forecasting Long-Term Crude Oil Prices Using a Bayesian Model with Informative Priors." Sustainability 9, no. 2: 190.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.