An empirical analysis of the dynamic interactions among ethanol, crude oil and corn prices in the US market

  • Dimitrios Dimitriadis
  • Constantinos Katrakilidis
S.I.: BALCOR-2017
First Online:
  • 6 Downloads

Abstract

This paper studies the dynamic interactions of the ethanol, crude oil and corn market prices in the United States from January 2005 up to December 2014. The empirical analysis, for the shake of robustness, employs alternative time series methodologies based on single equation and system estimation approaches to cointegration and more specifically, the autoregressive distributed lags and the Johansen ML cointegration methodologies were applied complementary. The findings reveal a statistically significant long-run causal effect running from crude oil and corn prices to ethanol price as well as from ethanol and corn prices to crude oil price. Additionally, a positive relationship between crude oil price and ethanol price was revealed.

Keywords

Ethanol Agricultural commodities Crude oil Corn Cointegration Causality 
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References

  1. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.CrossRefGoogle Scholar
  2. Akaike, H. (1976). Canonical correlation analysis of time series and the use of an information criterion. Mathematics in Science and Engineering, 126, 27–96.CrossRefGoogle Scholar
  3. Akaike, H. (1987). Factor analysis and AIC. Psychometrika, 52(3), 317–332.CrossRefGoogle Scholar
  4. Campiche, J. L., Bryant, H. L., Richardson, J. W., & Outlaw, J. L. (2007). Examining the evolving correspondence between petroleum prices and agricultural commodity prices. In The American agricultural economics association annual meeting, Portland, OR, July 29-August 1, 2007.Google Scholar
  5. Chiu, F., Hsu, C., Ho, A., & Chen, C. (2016). Modeling the price relationships between crude oil, energy crops and biofuels. Energy, 109, 845–857.CrossRefGoogle Scholar
  6. Hameed, A. A. A., & Arshad, F. A. (2008). The impact of the petroleum prices on vegetable oil prices: evidence from cointegration test. In Proceedings of the 3rd international borneo business conference (IBBC), pp. 504–514.Google Scholar
  7. Hertel, T., & Beckman, J. (2011). Commodity price volatility in the biofuel era (1st ed.). Cambridge, Mass: National Bureau of Economic Research.Google Scholar
  8. Hochman, G., Rajagopal, D., & Zilberman, D. (2010). The effect of biofuels on crude oil markets. AgBioForum, 13(2), 112–118.Google Scholar
  9. Irwin, S., & Good, D. (2009). Market instability in a new era of corn, soybean, and wheat prices. Agricultural & Applied Economics Association, 24(1), 6–11.Google Scholar
  10. Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in gaussian vector autoregressive models. Econometrica, 59(6), 1551.CrossRefGoogle Scholar
  11. Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration with applications to the demand for money. Oxford Bulletin of Economics and Statistics, 52(2), 169–210.CrossRefGoogle Scholar
  12. Muhammad, A., & Kebede, E. (2009). The emergence of an agro-energy sector: Is agriculture importing instability from the oil sector? Agricultural & Applied Economics Association, 24(1), 12–15.Google Scholar
  13. Narayan, P. K. (2004). Reformulating critical values for the bounds F-statistics approach to cointegration: An application to the tourism demand model for Fiji. Department of Economics discussion papers no. 02/04, Monash University, Melbourne, Australia.Google Scholar
  14. Pesaran, M., & Shin, Y. (1999). An autoregressive distributed lag modeling approach to cointegration analysis. In Econometrics and economic theory in the 20th century: The Ragnar frisch centennial symposium. Cambridge: Cambridge University Press.Google Scholar
  15. Pesaran, M., Shin, Y., & Smith, R. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289–326.CrossRefGoogle Scholar
  16. Romilly, P., Song, H., & Liu, X. (2001). Car ownership and use in Britain: a comparison of the empirical results of alternative cointegration estimation methods and forecasts. Applied Economics, 33(14), 1803–1818.CrossRefGoogle Scholar
  17. Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464.CrossRefGoogle Scholar
  18. Serra, T., Zilbermanb, D., Gila, J., & Goodwinc, B. (2011). Nonlinearities in the US corn-ethanol-oil-gasoline price system. Agricultural Economics, 42, 35–45.CrossRefGoogle Scholar
  19. Sumner, D. (2009). Recent commodity price movements in historical perspective. American Journal of Agricultural Economics, 91(5), 1250–1256.CrossRefGoogle Scholar
  20. Wright, B. (2011). The economics of grain price volatility. Applied Economic Perspectives and Policy, 33(1), 32–58.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Aristotle University of ThessalonikiThessalonikiGreece

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