Oil and U.S. GDP: A Real‐Time Out‐of‐Sample Examination
通过伪样本外预测练习，研究原油价格对美国实际GDP增长的实时预测能力，发现长期预测中石油价格具有显著预测力，但实时数据与事后修正数据的使用会导致模型表现差异。
We study the real‐time predictive content of crude oil prices for U.S. real GDP growth through a pseudo out‐of‐sample (OOS) forecasting exercise. Comparing our benchmark model “without oil” against alternatives “with oil,” we strongly reject the null hypothesis of no OOS population‐level predictability from oil prices to GDP at the longer forecast horizon we consider. This examination of the global OOS relative performance of the models we consider is robust to use of ex post revised data. But when we focus on the forecasting models’ local relative performance, we observe strong differences across use of real‐time and ex post revised data.