The past decade witnessed wide swings in commodity prices, which has spurred renewed interest in non-fuel commodities. In 2008 Ben Bernanke, then chairman of the US Federal Reserve, identified commodity prices as one of the main “outstanding issues in the analysis of inflation”. However, interpreting commodity price cycles and providing factor attribution is still an unsolved puzzle. Our project strives to shed some light on these issues. We plan to examine commodity price forecasting models, where predictors include fundamental, macroeconomic, and financial variables. We aim to systematically compare a large battery of different statistical models, where we also address model uncertainty. In comparing the competing models, we evaluate the forecast performance not only in terms of traditional measures but also in terms of new measures, including, e.g., indicators that assess whether the direction of the price change was correctly forecasted. Our main objective is to find out whether the quality of commodity price forecasts depends on the state of the economy (e.g., are commodity price predictions better in turbulent or in calm periods) and what variables are the key players in explaining different commodity classes in different states of the economy.