Influential factors in crude oil price forecasting |
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| Affiliation: | 1. International Economic Analysis Department, Bank of Canada, 234 Laurier Avenue West, Ottawa, ON K1A 0G9, Canada;2. Department of Economics, University of Michigan, 611 Tappan Street, Ann Arbor, MI 48109-1220, USA;3. U.S. Energy Information Administration, 1000 Independence Avenue, Washington, DC 20585, USA;1. School of Economics and Management, North China Electric Power University, Beijing 102206, PR China;2. Business School of Hunan University, Changsha 410082, PR China;3. Center for Resource and Environmental Management, Hunan University, Changsha 410082, PR China;4. Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, PR China;1. Institute of Policy and Management, Chinese Academy of Sciences, Beijing, China;2. University of Chinese Academy of Sciences, Beijing, China;3. School of Economics and Management, Beijing University of Chemical Technology, Beijing, China |
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| Abstract: | This paper identifies factors that are influential in forecasting crude oil prices. We consider six categories of factors (supply, demand, financial market, commodities market, speculative, and geopolitical) and test their significance in the context of estimating various forecasting models. We find that the Least Absolute Shrinkage and Selection Operator (LASSO) regression method provides significant improvements in the forecasting accuracy of prices compared to alternative benchmarks. Relative to the no-change and futures-based models, LASSO forecasts at the 8-step ahead horizon yield significant reductions in Mean Squared Prediction Error (MSPE), with MSPE ratios of 0.873 and 0.898, respectively. We also document substantial improvements in forecasting performance of the factor-based model that employs only a subset of variables chosen by LASSO. Finally, the time-varying nature of the relationship between factors and oil prices is used to explain recent movements in crude oil prices. |
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