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

Authors


  • We thank two anonymous referees, Christiane Baumeister, Hilde Bjornland, Efrem Castelnuovo, Todd Clark, Vincent Labhard, Lutz Kilian, Mike McCracken, Ken West, and seminar participants at the ECB, EUI, ICEEE 2011, SNDE 2011 conference, the Norges Bank conference on “Recent Developments in the Econometrics of Macroeconomics and Finance,” the 6th Eurostat Colloquium on “Modern Tools for Business Cycle Analysis: The Lessons from Global Economic Crisis,” and the University of Oslo conference on “Empirical Business Cycle Modelling and Policy in the Aftermath of the Financial Crisis” for helpful comments. We also thank Lutz Kilian for kindly providing his updated nominal index of bulk dry cargo ocean shipping freight rates. The views expressed in this paper are our own and do not necessarily reflect those of Norges Bank.

Abstract

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.

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