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.

衡量全球经济活动 (EA) 的指标通常用作宏观经济计量模型的输入。Baumeister and Hamilton (2019) 和 Hamilton (2019) 赞成使用世界工业生产指数作为全球 EA 的衡量标准。鉴于工业生产的变化与工业商品（例如原油）的需求之间存在联系，人们倾向于假设，如果原油价格变化为条件，则可以在样本外预测世界工业生产指数的变化油。有趣的是，相对于基准，我们没有发现任何证据表明我们基于原油价格的模型提高了样本外点预测的准确性。同样，Diebold 和 Mariano (1995) 建议的无条件相等预测能力测试很少表明在基准和基于原油价格的模型下产生的点预测之间存在统计差异。然而，Giacomini 和 White (2006) 中规定的同等条件预测能力的原假设经常被拒绝。通过依赖 Giacomini 和 White（2006 年）测试中使用的条件变量提供的信息，并按照 Granziera 和 Sekhposyan（2019 年）设计预测选择策略，我们成功地获得了提前一个月的预测准确度增益高达14%相对于基准。使用一年非对称净原油价格变化的非线性模型在业务状况不佳或股市不确定性较高时表现非常好。