EconPapers    
Economics at your fingertips  
 

Do Oil Prices Help Forecast U.S. Real GDP? The Role of Nonlinearities and Asymmetries

Lutz Kilian and Robert Vigfusson

Journal of Business & Economic Statistics, 2013, vol. 31, issue 1, 78-93

Abstract: There is a long tradition of using oil prices to forecast U.S. real GDP. It has been suggested that the predictive relationship between the price of oil and one-quarter-ahead U.S. real GDP is nonlinear in that (a) oil price increases matter only to the extent that they exceed the maximum oil price in recent years, and that (b) oil price decreases do not matter at all. We examine, first, whether the evidence of in-sample predictability in support of this view extends to out-of-sample forecasts. Second, we discuss how to extend this forecasting approach to higher horizons. Third, we compare the resulting class of nonlinear models to alternative economically plausible nonlinear specifications and examine which aspect of the model is most useful for forecasting. We show that the asymmetry embodied in commonly used nonlinear transformations of the price of oil is not helpful for out-of-sample forecasting; more robust and often more accurate real GDP forecasts are obtained from symmetric nonlinear models based on the 3-year net oil price change. Finally, we quantify the extent to which the 2008 recession could have been forecast using the latter class of time-varying threshold models.

Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (112)

Downloads: (external link)
http://hdl.handle.net/10.1080/07350015.2012.740436 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Do Oil Prices Help Forecast U.S. Real GDP? The Role of Nonlinearities and Asymmetries (2012) Downloads
Working Paper: Do oil prices help forecast U.S. real GDP? the role of nonlinearities and asymmetries (2012) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:31:y:2013:i:1:p:78-93

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UBES20

DOI: 10.1080/07350015.2012.740436

Access Statistics for this article

Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan

More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-04-14
Handle: RePEc:taf:jnlbes:v:31:y:2013:i:1:p:78-93