Oil price forecasts have traditionally attracted the interest of both the empirical literature and policy makers, although research efforts have been intensified in the last 15 years. The present study investigates the forecasting characteristics that have the greatest impact on the accuracy level of such forecasts. To achieve this, we employ a meta-analysis approach of more than 6,000 observations of relative root mean squared errors (RRMSEs) which are pooled within a Bayesian Model Averaging (BMA) method. The findings indicate that forecasting frameworks such as MIDAS and combined forecasts tend to report significantly lower forecast errors. In addition, the choice of the oil price benchmark is an important factor, with the Brent price to offer lower forecast errors. Furthermore, the short-run horizons tend to produce more accurate forecasts and the same holds for the real, instead of the nominal oil prices. A number of robustness tests confirms the validity of these results. Overall, the findings of this study serve as a guide for future oil price forecasting exercises.
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