Forecasting Industrial Commodity Prices
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World Bank, Washington, DC
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Almost two-thirds of emerging market
and developing economies rely heavily on resource sectors
for economic activity, fiscal and export revenues. In these
economies, economic planning requires sound baseline
projections for the global prices of the commodities they
rely on and a sense of the risks around such baseline
projections. This paper presents a model suite to prepare
well-founded forecasts for the global prices for oil and six
industrial metals (aluminum, copper, lead, nickel, tin, and
zinc). The model suite adapts six approaches used in the
literature and tests their forecast performance. Broadly
speaking, futures prices or bivariate correlations performed
well at short horizons, and consensus forecasts and a
large-scale macroeconometric model performed well at long
horizons. The strength of Bayesian vector autoregression
models lies in generating forecast scenarios. The sizable
forecast error bands generated by the model suite highlight
the need for policy makers to engage in careful contingency
planning for higher or lower prices.
Palabras clave
OIL PRICE, METAL PRICE, COMMODITY PRICES, FORECASTING