Oil and U.S. GDP: A Real-Time Out-of-Sample Examination

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 populati...

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Bibliographic Details
Published in:Journal of money, credit and banking credit and banking, 2013-03, Vol.45 (2-3), p.449-463
Main Authors: RAVAZZOLO, FRANCESCO, ROTHMAN, PHILIP
Format: Article
Language:English
Subjects:
Benchmarking
C22
C53
Crude oil
Crude oil prices
E32
E37
Economic forecasting
Economic forecasts
Economic growth
Economic growth models
Economic models
Economic performance
forecasting
Forecasting models
GDP
Gross Domestic Product
Import prices
Modeling
oil
Oil price
Oil prices
Predictability
Real gross domestic product
real-time data
recessions
SHORTER PAPERS, DISCUSSIONS, AND LETTERS
Statistical forecasts
Studies
U.S.A
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Summary: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.
ISSN:0022-2879
1538-4616
DOI:10.1111/jmcb.12009