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Sustainability 2018, 10(2), 454;

Improving the Forecasting Accuracy of Crude Oil Prices

The College of Finance and Statistics, Hunan University, Changsha 410006, China
Author to whom correspondence should be addressed.
Received: 23 January 2018 / Revised: 6 February 2018 / Accepted: 6 February 2018 / Published: 9 February 2018
(This article belongs to the Section Energy Sustainability)
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Currently, oil is the key element of energy sustainability, and its prices and economy have a strong mutual influence. Modeling a good method to accurately predict oil prices over long future horizons is challenging and of great interest to investors and policymakers. This paper forecasts oil prices using many predictor variables with a new time-varying weight combination approach. In doing so, we first use five single-variable time-varying parameter models to predict crude oil prices separately. Second, every special model is assigned a time-varying weight by the new combination approach. Finally, the forecasting results of oil prices are calculated. The results show that the paper’s method is robust and performs well compared to random walk. View Full-Text
Keywords: forecast; time-varying weight; Kalman filter; random walk forecast; time-varying weight; Kalman filter; random walk

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yin, X.; Peng, J.; Tang, T. Improving the Forecasting Accuracy of Crude Oil Prices. Sustainability 2018, 10, 454.

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