Oil and US GDP: A Real-Time Out-of Sample Examination
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We study the real-time predictive content of crude oil prices for US 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. These results may be due to our oil price measures serving as proxies for a recently developed measure of global real economic activity omitted from the alternatives to the benchmark forecasting models. 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.
1/2010 and 2/2010 was published as CAMAR Working Papers Series (ISSN 1892-2198). From 2011 the series' name changed to CAMP Working Paper Series.