This paper seeks to produce forecasts of commodity price movements that can systematically improve on naïve statistical benchmarks. Groen and Pesenti revisit how well changes in commodity currencies perform as potential efficient predictors of commodity prices, a view emphasized in the recent literature. They also consider different types of factor-augmented models that use information from a large data set containing a variety of indicators of supply and demand conditions across major developed and developing countries. These models use either standard principal components or the more novel partial least squares (PLS) regression to extract dynamic factors from the data set. The authors consider ten alternative indices and sub-indices of spot prices for three different commodity classes across different periods. They find that of all the approaches, the exchange-rate–based model and the PLS factor-augmented model are more likely to outperform the naïve statistical benchmarks, although PLS factor-augmented models usually have a slight edge over the exchange-rate–based approach.