Combining Forecasts for the Price of Oil: Application and Evaluation of Methodologies


This paper conducts an exhaustive out-of-sample forecasting evaluation exercise for the monthly price of crude oil between 1992 and 2011. The idea is to identify the forecasting strategy that results in the “best” forecasts in terms of mean forecasting error. To this end, a wide variety of econometric models as well as future prices are tested for different forecasting horizons in an individual manner, as well as combined. We find that for short horizons (1 and 3 months), an ARIMA specification results in smaller forecasting errors, but for longer horizons (6-24 months), future prices outperform other models. All models are found to underestimate the true price of oil, on average. The combination of these individual models only yields smaller forecasting errors when compared to the “best” individual strategy in a restricted sample ending in 2005. Nevertheless, when we tabulate the number of times one strategy yields the largest forecasting error compared to other alternatives, combinations of forecasts never yields the highest absolute error except one month ahead. These results are robust to the sample selection.

Working Paper N°660 Central Bank of Chile [In Spanish]
Mariel Siravegna
Mariel Siravegna
Ph.D. Candidate in Economics

I am a Ph.D. candidate at the Georgetown University, graduating in June 2021. My fields of specialization are labor economics and applied econometrics.