Forecasting the Nominal Brent Oil Price with VARs-One Model Fits All?

33 Pages Posted: 3 Feb 2016

See all articles by Benjamin Beckers

Benjamin Beckers

German Institute for Economic Research (DIW Berlin)

Samya Beidas-Strom

International Monetary Fund (IMF)

Date Written: November 2015

Abstract

We carry out an ex post assessment of popular models used to forecast oil prices and propose a host of alternative VAR models based on traditional global macroeconomic and oil market aggregates. While the exact specification of VAR models for nominal oil price prediction is still open to debate, the bias and underprediction in futures and random walk forecasts are larger across all horizons in relation to a large set of VAR specifications. The VAR forecasts generally have the smallest average forecast errors and the highest accuracy, with most specifications outperforming futures and random walk forecasts for horizons up to two years. This calls for caution in reliance on futures or the random walk for forecasting, particularly for near term predictions. Despite the overall strength of VAR models, we highlight some performance instability, with small alterations in specifications, subsamples or lag lengths providing widely different forecasts at times. Combining futures, random walk and VAR models for forecasting have merit for medium term horizons.

Keywords: VARs, forecasts, prices, random walk, demand, Time-Series Models, Forecasting and Other Model Applications, Energy and the Macroeconomy, All Countries,

JEL Classification: C53, Q43, C32

Suggested Citation

Beckers, Benjamin and Beidas-Strom, Samya, Forecasting the Nominal Brent Oil Price with VARs-One Model Fits All? (November 2015). IMF Working Paper No. 15/251. Available at SSRN: https://ssrn.com/abstract=2727216

Benjamin Beckers (Contact Author)

German Institute for Economic Research (DIW Berlin) ( email )

Mohrenstra├če 58
Berlin, 10117
Germany

Samya Beidas-Strom

International Monetary Fund (IMF) ( email )

700 19th Street, N.W.
Washington, DC 20431
United States

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