The price of crude oil and (conditional) out-of-sample predictability of world industrial production

A measure of global economic activity (EA) is often used as input in macroeconometric models. Baumeister and Hamilton (2019) and Hamilton (2019) favor using the world industrial production index as a measure for global EA. Given the connections between variations in industrial production and demand for industrial commodities, such as crude oil, one is inclined to assume that changes in the world industrial production index can be predicted out-of-sample if one conditions on changes in the price of crude oil. Interestingly, we do not find any evidence of out-of-sample point forecast accuracy gains from our crude oil price-based models relative to the benchmark. Likewise, the unconditional equal predictive ability test suggested in Diebold and Mariano (1995) rarely indicates a statistical difference between point forecasts produced under the benchmark and the crude oil price-based models. However, the null hypothesis of equal conditional predictive ability as specified in Giacomini and White (2006) is often rejected. By relying on the information provided by the conditioning variables used in the Giacomini and White (2006) test, and devising a forecast selection strategy following Granziera and Sekhposyan (2019), we succeed at obtaining one-month ahead point forecast accuracy gains as high as 14% relative to the benchmark. The nonlinear model using the one-year asymmetric net crude oil price change performs very well when business conditions are bad or equity market uncertainty is high.