Forecasting Long-Term Crude Oil Prices Using a Bayesian Model with Informative Priors

• Main Authors: Chul-Yong Lee, Sung-Yoon Huh
• Format: Article
• Language: English
• Published: MDPI AG 2017-01-01
• Series: Sustainability
• Subjects: Bayesian estimation; oil price; forecasting model; informative priors
• Online Access: http://www.mdpi.com/2071-1050/9/2/190

In 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.