Third, BVAR models had significantly poorer forecast performance than all other approaches, in terms of bias, precision, and direction of change, for all commodities and all horizons. The strength of BVAR models lies not so much in forecast performance, but in scenario analysis: among the approaches considered here, BVAR models allow the most straightforward translation of global output growth forecasts into industrial commodity price forecasts ( **Table 13** ).

There are some caveats on the comparison. Broadly speaking, these caveats bias the comparison against the BVAR approach. First, future prices, consensus forecasts, and the machine learning approach directly predict nominal price levels while bivariate correlations forecast percent changes of nominal prices. BVAR models predict real prices, then inflate nominal prices using actual inflation rates. Second, all the models except for the BVAR approach generate monthly price forecasts that are aggregated into quarterly forecasts for comparison; in contrast, the BVAR uses the quarterly average of monthly data as input to generate quarterly price forecasts since GDP data is only available at the quarterly frequency. Third and most important, forecasts were done without any judgement. None of the forecasting approaches here have pre-designed scenarios or priors. However, in reality, it is often of interest to condition the forecasts on different scenarios. The OEM is particularly useful to conduct scenario exercises that take into account changes in policy variables, global growth, inflation, and structural variables. Another purpose of conditional forecasts is to incorporate information from higher frequency data or judgment into the model (Karlsson 2013). This underscores the main advantage of the BVAR model, which allows the forecaster to simulate scenarios or test priors while maintaining the statistical properties of model. The forecaster can then make inferences about the posterior forecast conditional on the prior. The version of the BVAR model used in this exercise leads to a larger forecast bias than other models for oil prices. However, in practice, the forecaster could subsequently adjust the parameters to reflect changes in priors to arrive at more informed forecasts.

# **5. Conclusion**

This paper presents a model suite for forecasting prices of seven industrial commodities: oil, aluminum, copper, lead, nickel, tin, and zinc. It includes six approaches, based on the review of a rich literature of commodity price forecasting: consensus forecasts, futures prices, univariate correlation, a Bayesian VAR, a large-scale macroeconometric model ( _Oxford Economic Model_ ), and a machine learning-based approach (EMD-based SVR and GARCH).

It finds that no single approach is the best but rather that model performance depends on the commodity and the forecast horizon. As a rule, futures prices or bivariate correlations performed well at short horizons; consensus forecasts and a macroeconometric model at long horizons. The strength of a BVAR model lies in its ready applicability to forecast scenarios and the incorporation of external knowledge.

While these approaches are useful to anchor forecasts, they will always need to be supplemented by judgment. For policy makers, the wide range of forecasts and forecast errors is a reminder of the uncertainty around commodity price forecasts and the need to develop contingency plans for alternative outcomes.

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