Working Paper: CEPR ID: DP9569
Authors: Christiane Baumeister; Lutz Kilian
Abstract: The U.S. Energy Information Administration regularly publishes short-term forecasts of the price of crude oil. Traditionally, such out-of-sample forecasts have been largely judgmental, making them difficult to replicate and justify, and not particularly successful when compared with naïve no-change forecasts, as documented in Alquist et al. (2013). Recently, a number of alternative econometric oil price forecasting models has been introduced in the literature and shown to be more accurate than the no-change forecast of the real price of oil. We investigate the merits of constructing real-time forecast combinations of six such models with weights that reflect the recent forecasting success of each model. Forecast combinations are promising for four reasons. First, even the most accurate forecasting models do not work equally well at all times. Second, some forecasting models work better at short horizons and others at longer horizons. Third, even the forecasting model with the lowest MSPE may potentially be improved by incorporating information from other models with higher MSPE. Fourth, one can think of forecast combinations as providing insurance against possible model misspecification and smooth structural change. We demonstrate that over the last 20 years suitably constructed real-time forecast combinations would have been more accurate than the no-change forecast at every horizon up to two years. Relative to the no-change forecast, forecast combinations reduce the mean-squared prediction error by up to 18%. They also have statistically significant directional accuracy as high as 77%. We conclude that suitably constructed forecast combinations should replace traditional judgmental forecasts of the price of oil.
Keywords: forecast combination; model misspecification; oil price; real-time data; structural change
JEL Codes: C53; E32; Q43
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| suitably constructed real-time forecast combinations of six econometric models (C53) | outperform the traditional no-change forecast (C53) |
| forecast combinations (C53) | reduce the mean squared prediction error (MSPE) (C51) |
| forecast combinations (C53) | achieve directional accuracy rates as high as 77% (C52) |
| various forecasting models (C53) | influence the accuracy of oil price predictions (Q47) |
| inverse MSPE weights (C51) | allow for a dynamic response to model performance over time (C22) |