Improving the Forecasting Accuracy of Crude Oil Prices
Xuluo Yin (),
Jiangang Peng () and
Tian Tang ()
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Xuluo Yin: The College of Finance and Statistics, Hunan University, Changsha 410006, China
Jiangang Peng: The College of Finance and Statistics, Hunan University, Changsha 410006, China
Tian Tang: The College of Finance and Statistics, Hunan University, Changsha 410006, China
Sustainability, 2018, vol. 10, issue 2, 1-9
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.
Keywords: forecast; time-varying weight; Kalman filter; random walk (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:10:y:2018:i:2:p:454-:d:131091
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