Advanced search
1 file | 264.76 KB

Real-time forecasts of the real price of oil

Author
Organization
Abstract
We construct a monthly real-time dataset consisting of vintages for 1991.1-2010.12 that is suitable for generating forecasts of the real price of oil from a variety of models. We document that revisions of the data typically represent news, and we introduce backcasting and nowcasting techniques to fill gaps in the real-time data. We show that real-time forecasts of the real price of oil can be more accurate than the no-change forecast at horizons up to 1 year. In some cases, real-time mean squared prediction error (MSPE) reductions may be as high as 25% 1 month ahead and 24% 3 months ahead. This result is in striking contrast to related results in the literature for asset prices. In particular, recursive vector autoregressive (VAR) forecasts based on global oil market variables tend to have lower MSPE at short horizons than forecasts based on oil futures prices, forecasts based on autoregressive (AR) and autoregressive moving average (ARMA) models, and the no-change forecast. In addition, these VAR models have consistently higher directional accuracy.
Keywords
Vector autoregression, Real time data, Out-of-sample prediction, Futures, Oil price

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 264.76 KB

Citation

Please use this url to cite or link to this publication:

Chicago
Baumeister, Christiane, and Lutz Kilian. 2012. “Real-time Forecasts of the Real Price of Oil.” Journal of Business & Economic Statistics 30 (2): 326–336.
APA
Baumeister, C., & Kilian, L. (2012). Real-time forecasts of the real price of oil. JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 30(2), 326–336.
Vancouver
1.
Baumeister C, Kilian L. Real-time forecasts of the real price of oil. JOURNAL OF BUSINESS & ECONOMIC STATISTICS. 2012;30(2):326–36.
MLA
Baumeister, Christiane, and Lutz Kilian. “Real-time Forecasts of the Real Price of Oil.” JOURNAL OF BUSINESS & ECONOMIC STATISTICS 30.2 (2012): 326–336. Print.
@article{2075171,
  abstract     = {We construct a monthly real-time dataset consisting of vintages for 1991.1-2010.12 that is suitable for generating forecasts of the real price of oil from a variety of models. We document that revisions of the data typically represent news, and we introduce backcasting and nowcasting techniques to fill gaps in the real-time data. We show that real-time forecasts of the real price of oil can be more accurate than the no-change forecast at horizons up to 1 year. In some cases, real-time mean squared prediction error (MSPE) reductions may be as high as 25\% 1 month ahead and 24\% 3 months ahead. This result is in striking contrast to related results in the literature for asset prices. In particular, recursive vector autoregressive (VAR) forecasts based on global oil market variables tend to have lower MSPE at short horizons than forecasts based on oil futures prices, forecasts based on autoregressive (AR) and autoregressive moving average (ARMA) models, and the no-change forecast. In addition, these VAR models have consistently higher directional accuracy.},
  author       = {Baumeister, Christiane and Kilian, Lutz},
  issn         = {0735-0015},
  journal      = {JOURNAL OF BUSINESS \& ECONOMIC STATISTICS},
  keyword      = {Vector autoregression,Real time data,Out-of-sample prediction,Futures,Oil price},
  language     = {eng},
  number       = {2},
  pages        = {326--336},
  title        = {Real-time forecasts of the real price of oil},
  url          = {http://dx.doi.org/10.1080/07350015.2011.648859},
  volume       = {30},
  year         = {2012},
}

Altmetric
View in Altmetric
Web of Science
Times cited: