Working Paper: CEPR ID: DP10075
Authors: Christiane Baumeister; Lutz Kilian; Thomas K. Lee
Abstract: The answer depends on the objective. The approach of combining five of the leading forecasting models with equal weights dominates the strategy of selecting one model and using it for all horizons up to two years. Even more accurate forecasts, however, are obtained when allowing the forecast combinations to vary across forecast horizons. While the latter approach is not always more accurate than selecting the single most accurate forecasting model by horizon, its accuracy can be shown to be much more stable over time. The MSPE of real-time pooled forecasts is between 3% and 29% lower than that of the no-change forecast and its directional accuracy as high as 73%. Our results are robust to alternative oil price measures and apply to monthly as well as quarterly forecasts. We illustrate how forecast pooling may be used to produce real-time forecasts of the real and the nominal price of oil in a format consistent with that employed by the U.S. Energy Information Administration in releasing its short-term oil price forecasts, and we compare these forecasts during key historical episodes.
Keywords: forecast combination; forecast pooling; oil price; real-time data; refiners acquisition cost; WTI
JEL Codes: C53; Q43
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Pooling forecasts from multiple models (C53) | Reduction in mean squared prediction error (MSPE) (C51) |
| Pooling forecasts from multiple models (C53) | Higher directional accuracy (C52) |
| Allowing forecast combinations to vary across horizons (C53) | More accurate forecasts (C53) |
| Pooled forecasts (C53) | Improved forecast outcomes compared to US EIA forecasts (Q47) |