Jump to ContentJump to Main Navigation
Show Summary Details

Studies in Nonlinear Dynamics & Econometrics

Ed. by Mizrach, Bruce

5 Issues per year

IMPACT FACTOR increased in 2015: 0.517
5-year IMPACT FACTOR: 0.628

SCImago Journal Rank (SJR) 2015: 0.426
Source Normalized Impact per Paper (SNIP) 2015: 0.546
Impact per Publication (IPP) 2015: 0.419

Mathematical Citation Quotient (MCQ) 2015: 0.01

See all formats and pricing
Volume 20, Issue 4 (Sep 2016)


Oil-price density forecasts of US GDP

Francesco Ravazzolo
  • Free University of Bozen-Bolzano, Faculty of Economics and Management, 39100 Bozen-Bolzano, Italy
/ Philip Rothman
  • Corresponding author
  • Brewster A-424, Department of Economics East, Carolina University, Greenville, NC 27858-4353, USA, Phone: (252) 328-6151
  • Email:
Published Online: 2016-06-11 | DOI: https://doi.org/10.1515/snde-2015-0116


We carry out a pseudo out-of-sample density forecasting study for US GDP with an autoregressive benchmark and alternatives to the benchmark that include both oil prices and stochastic volatility. The alternatives to the benchmark produce superior density forecasts. This comparative density performance appears to be driven more by stochastic volatility than by oil prices, and it primarily occurs outside of the great recession. We use our density forecasts to compute a recession risk indicator around the great recession. The alternative model with the real price of oil generates the earliest strong signal of a recession; but it surprisingly indicates reduced recession immediately after the Lehman Brothers bankruptcy. Use of the “net oil-price increase” nonlinear transformation of oil prices does lead to warnings of highly elevated risk during the Great Recession.

This article offers supplementary material which is provided at the end of the article.

Keywords: business cycle; density forecasts; GDP; oil prices

JEL Classification: C22; C53; E32; E37


  • Alessi, L., E. Ghysels, L. Onorante, R. Peach, and S. Potter. 2014. “Central Bank Macroeconomic Forecasting During the Global Financial Crisis: The European Central Bank and Federal Reserve Bank of New York Experiences.” Journal of Business & Economic Statistics 32: 483–500.

  • Alquist, R., L. Kilian, and R. J. Vigfusson. 2013. “Handbook of Economic Forecasting.” In: Forecasting the Price of Oil, edited by G. Elliott and A. Timmermann, vol. 2, 427–507. Amsterdam: North Holland.

  • Bachmeier, L., Q. Li, and D. Liu. 2008. “Should Oil Prices Receive So Much Attention? An Evaluation of the Predictive Power of Oil Prices for the U.S. Economy.” Economic Inquiry 46: 528–539.

  • Barsky, R. B., and L. Kilian. 2002. NBER Macroeconomics Annual 2001. In: Do We Really Know That Oil Caused the Great Stagflation? A Monetary Alternative, edited by B. S. Bernanke and K. Rogoff. Cambridge, MA: MIT Press.

  • Barsky, R. B., and L. Kilian. 2004. “Oil and the Macroeconomy Since the 1970s.” Journal of Economic Perspectives 18: 115–134.

  • Baumeister, C., and L. Kilian. 2011. “Real-Time Forecasts of the Real Price of Oil.” Journal of Business & Economic Statistics 30: 326–336.

  • Baumeister, C., and G. Peersman. 2013. “Time-Varying Effects of Oil Supply Shocks on the US Economy.” American Economic Journal: Macroeconomics 5: 1–28.

  • Berge, T., E. Elias, and O. Jordá. 2011. “Future Recession Risks: An Update.” Federal Reserve Bank of San Francisco Economic Letter 2011–35.

  • Bernanke, B. S., M. Gertler, and M. W. Watson. 1997. “Systematic Monetary Policy and The Effects of Oil Price Shocks.” Brookings Papers on Economic Activity 91–142.

  • Britton, E., P. Fisher, and J. Whitley. 1998. “The Inflation Report Projections: Understanding the Fan Chart.” Bank of England Quarterly Bulletin 38: 30–37.

  • Clark, T. E. 2011. “Real-Time Density Forecasts From Bayesian Vector Autoregressions With Stochastic Volatility.” Journal of Business & Economic Statistics 29: 327–341.

  • Clark, T. E., and M. W. McCracken. 2009. “Tests of Equal Predictive Ability With Real-Time Data.” Journal of Business & Economic Statistics 27: 441–454.

  • Clark, T. E., and M. W. McCracken. 2011. “Advances in Forecast Evaluation.” Working papers 2011-025, Federal Reserve Bank of St. Louis.

  • Clark, T. E., and F. Ravazzolo. 2015. “Macroeconomic Forecasting Performance Under Alternative Specifications of Time-Varying Volatility.” Journal of Applied Econometrics 30: 551–575.

  • Diebold, F. X., and R. S. Mariano. 1995. “Comparing Predictive Accuracy.” Journal of Business & Economic Statistics 13: 253–63.

  • Diebold, F., A. Gunther, and K. Tay. 1998. “Evaluating Density Forecasts With Applications to Financial Risk Management.” International Economic Review 39: 863–883.

  • Edelstein, P., and L. Kilian. 2009. “How Sensitive Are Consumer Expenditures to Retail Energy Prices?” Journal of Monetary Economics 56: 766–779. [Web of Science]

  • Garratt, A., K. Lee, M. H. Pesaran, and Y. Shin. 2003. “Forecast Uncertainties in Macroeconomic Modeling: An Application to the UK Economy.” Journal of the American Statistical Association 98: 829–38.

  • Geweke, J., and G. Amisano. 2010. “Comparing and Evaluating Bayesian Predictive Distributions of Asset Returns.” International Journal of Forecasting 26: 216–230.

  • Gneiting, T. 2011. “Making and Evaluating Point Forecasts.” Journal of the American Statistical Association 106: 746–762.

  • Gneiting, T., and A. Raftery. 2007. “Strictly Proper Score Rules, Prediction, and Estimation.” Journal of the American Statistical Association 102: 359–378.

  • Gneiting, T., and R. Ranjan. 2011. “Comparing Density Forecasts Using Threshold and Quantile Weighted Proper Scoring Rules.” Journal of Business & Economic Statistics 29: 411–422.

  • Hamilton, J. D. 1983. “Oil and the Macroeconomy Since World War II.” Journal of Political Economy 91: 228–248.

  • Hamilton, J. D. 1996. “This is What Happened to the Oil Price-Macroeconomy Relationship.” Journal of Monetary Economics 38: 225–230.

  • Hamilton, J. D. 2003. “What is an Oil Shock?” Journal of Econometrics 113: 363–398.

  • Hamilton, J. D. 2009. “Causes and Consequences of the Oil Shock of 2007–08.” Brookings Papers on Economic Activity 215–259.

  • Hamilton, J. D., and A. M. Herrera. 2004. “Oil Shocks and Aggregate Macroeconomic Behavior: The Role of Monetary Policy.” Journal of Money, Credit, and Banking 36: 265–286.

  • Hooker, M. 1996. “What Happened to the Oil Price-Macroeconomy Relationship?” Journal of Monetary Economics 38: 195–213.

  • Kilian, L. 2008. “The Economic Effects of Energy Price Shocks.” Journal of Economic Literature 46: 871–909.

  • Kilian L. 2009. “Not all Oil Price Shocks are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market.” American Economic Review 99: 1053–1069.

  • Kilian L. 2010. “Oil Price Shocks, Monetary Policy and Stagflation.” In: Inflation in an Era of Relative Price Shocks, RBA Annual Conference Volume, edited by R. Fry, C. Jones, and C. Kent. Sydeny, Australia: Reserve Bank of Australia.

  • Kilian, L., and S. Manganelli. 2008. “The Central Banker as a Risk Manager: Estimating the Federal Reserve’s Preferences Under Greenspan.” Journal of Money, Credit and Banking 40: 1103–1129.

  • Kilian, L., and R. Vigfusson. 2011a. “Nonlinearities in the Oil Price-Output Relationship.” Macroeconomic Dynamics 15: 337–363.

  • Kilian, L., and R. J. Vigfusson. 2011b. “Are the Responses of the U.S. Economy Asymmetric in Energy Price Increases and Decreases?” Quantitative Economics 2: 419–453.

  • Kilian, L., and R. J. Vigfusson. 2013. “Do Oil Prices Help Forecast U.S. Real GDP? The Role of Nonlinearities and Asymmetries.” Journal of Business & Economic Statistics 31: 78–93.

  • Kim, S., N. Shephard, and S. Chib. 1998. “Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models.” Review of Economic Studies 65: 361–393.

  • Koenig, E. F., S. Dolmas, and J. Piger. 2003. “The Use and Abuse of Real-Time Data in Economic Forecasting.” The Review of Economics and Statistics 85: 618–628.

  • Koop, G. 2003. Bayesian Econometrics, Hoboken, NJ, USA: John Wiley and Sons.

  • Mitchell, J., and S. Hall. 2005. “Evaluating, Comparing and Combining Density Forecasts Using the KLIC With an Application to the Bank of England and NIESER ‘Fan’ Charts of Inflation.” Oxford Bulletin of Economics and Statistics 67: 995–1033.

  • Mork, K. A. 1989. “Oil and Macroeconomy When Prices Go Up and Down: An Extension of Hamilton’s Results.” Journal of Political Economy 97: 740–44.

  • Omori, Y., S. Chib, N. Shephard, and J. Nakajima. 2007. “Stochastic Volatility with Leverage: Fast and Efficient Likelihood Inference.” Journal of Econometrics 140: 425–449.

  • Primiceri, G. 2005. “Time Varying Structural Vector Autoregressions and Monetary Policy.” Review of Economic Studies 72: 821–852.

  • Ravazzolo, F., and P. Rothman. 2013. “Oil and U.S. GDP: A Real-Time Out-of-Sample Examination.” Journal of Money, Credit, and Banking 45: 449–463.

  • Ravazzolo, F., and S. Vahey. 2014. “Forecast Densities for Economic Aggregates from Disaggregate Ensembles.” Studies of Nonlinear Dynamics and Econometrics 18: 367–381.

  • Tay, A., and K. F. Wallis. 2000. “Density Forecasting: A Survey.” Journal of Forecasting 19: 235–254.

About the article

Published Online: 2016-06-11

Published in Print: 2016-09-01

Citation Information: Studies in Nonlinear Dynamics & Econometrics, ISSN (Online) 1558-3708, ISSN (Print) 1081-1826, DOI: https://doi.org/10.1515/snde-2015-0116. Export Citation

Supplementary Article Materials

Comments (0)

Please log in or register to comment.
Log in