Miao, Hong, authorRamchander, Sanjay, authorWang, Tianyang, authorYang, Dongxiao, authorEnergy Economics, publisher2020-05-122020-05-122017-07-12Miao, H., Ramchander, S., Wang, T., & Yang, D. (2017). Influential factors in crude oil price forecasting. Energy Economics, 68. https://doi.org/10.1016/j.eneco.2017.09.010https://hdl.handle.net/10217/206713Includes bibliographical references (pages 23-26).Published as: Energy Economics, vol. 68, October 2017, pp. 77-78, https://doi.org/10.1016/j.eneco.2017.09.010.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.born digitalarticleseng©2020 Elsevier B.V. Author can archive pre-print and post-print.Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.oil pricesforecastingleast absolute shrinkage and selection operator (LASSO)mean squared prediction error (MSPE)success ratioText