Price Transmission in the US Ethanol Market

  • Teresa Serra
  • David Zilberman
  • José M. Gil
  • Barry K. Goodwin
Part of the Natural Resource Management and Policy book series (NRMP, volume 33)


We use nonlinear time series models to assess price relationships within the US ethanol industry. Daily ethanol, corn, and crude oil futures prices observed from mid-2005 to mid-2007 are used in the analysis. Our results suggest the existence of an equilibrium relationship between the three prices studied. Only ethanol prices are found to adjust to deviations from this relationship. The evolution of ethanol prices in relation to corn and crude oil prices may have important implications for the long-run competitiveness of the US ethanol industry.


Future Price Cointegration Relationship Price Transmission Ethanol Industry Corn Price 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors gratefully acknowledge financial support from Instituto Nacional de Investigaciones Agrícolas (INIA) and the European Regional Development Fund (ERDF), Plan Nacional de Investigación Científica, Desarrollo e Innovación Tecnológica (I+D+i), Project Reference Number RTA2009-00013-00-00.


  1. Azzam A M (1999) Asymmetry in rigidity in farm-retail price transmission. Am J Agri Econ, 813): 525–533.CrossRefGoogle Scholar
  2. Balcombe K, Bailey A, Brooks J (2007) Threshold effects in price transmission: the case of Brazilian wheat, maize and soya prices. Am J Agri Econ 89(2): 308–323.CrossRefGoogle Scholar
  3. Balcombe K, Rapsomanikis G (2008) Bayesian estimation of nonlinear vector error correction models: the case of sugar-ethanol-oil nexus in Brazil. Am J Agri Econ 90(3): 658–668.CrossRefGoogle Scholar
  4. Booth G G, Tse Y (1995) Long memory in interest rate futures markets: A fractional cointegration analysis. J Futures Markets 15(5): 573–584.CrossRefGoogle Scholar
  5. Chan K S, Tong H (1986) On estimating thresholds in autoregressive models. J Time Ser Anal 7(3): 179–190.CrossRefGoogle Scholar
  6. Chavas J P, Metha A (2004) Price dynamics in a vertical sector: The case of butter.Am J Agri Econ 86(4): 1078–1093.CrossRefGoogle Scholar
  7. CME Group (2007) Ethanol Derivatives, Key Charts and Data. Available at
  8. de Gorter H, Just D (2009) The welfare economics of a biofuel tax credit and the interaction effects with price contingent farm subsidies. Am J Agri Econ 91(2): 477–488CrossRefGoogle Scholar
  9. Dickey D A, Fuller W A (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 74(336): 427–431.CrossRefGoogle Scholar
  10. Eitrheim Ø, Teräsvirta T (1996) Testing the adequacy of smooth transition autoregressive models. J Econometrics 74(1): 59–75.CrossRefGoogle Scholar
  11. Energy Information Administration (EIA) (2007) US Energy Information Administration Before the Committee on Agriculture. Testimony of Dr. Howard Gruenspecth Deputy Administrator. Available at
  12. Goodwin B K, Piggott N E (2001) Spatial marketing integration in the presence of threshold effects. Am J Agri Econ 83(1): 302–317.CrossRefGoogle Scholar
  13. Granger C W J (1969) Investigating causal relations by econometric methods and cross-spectral methods. Econometrica 37(3): 424–438.CrossRefGoogle Scholar
  14. Hansen H, Johansen S (1999) Some tests for parameter constancy in cointegrated VAR-models. Econometrics J 2(2): 306–333.CrossRefGoogle Scholar
  15. Jin H J, Frechette D L (2004) Fractional integration in agricultural futures price volatilities. Am J Agri Econ 86(2): 432–443.CrossRefGoogle Scholar
  16. Johansen S (1988) Statistical analysis of cointegrating vectors. J Econ Dynamics Control 12(2–3): 231–254.CrossRefGoogle Scholar
  17. Koop G, Pesaran M H, Potter S M (1996) Impulse response analysis in nonlinear multivariate models. J Econometrics 74(1): 119–147.CrossRefGoogle Scholar
  18. Koplow D (2006) Biofuels – at what cost? Government support for ethanol and biodiesel in the United States. International Institute for Sustainable Development, Geneva, Switzerland. Available at
  19. Kwiatkowski D, Phillips P C B, Schmidt P, Shin Y (1992) Testing the null hypothesis of stationarity against the alternative of a unit root. J Econometrics 54(1–3): 159–178.CrossRefGoogle Scholar
  20. Lloyd T A, McCorriston S, Morgan C W, Rayner, A J (2006) Food scares, market power and price transmission: the UK BSE crisis. European Rev Agri Econ 33(2): 119–147.CrossRefGoogle Scholar
  21. McCorriston S, Morgan C W, Rayner A J (1998) Processing technology, market power and price transmission. J Agri Econ 49(2): 185–201.CrossRefGoogle Scholar
  22. McCorriston S, Morgan C W, Rayner A J (2001) Price transmission: The interaction between market power and returns to scale. Eur Rev Agri Econ 28(2): 143–159.CrossRefGoogle Scholar
  23. OECD, Directorate for Food, Agriculture and Fisheries, Committee for Agriculture (2006) Agricultural Market Impacts of Future Growth in the Production of Biofuels. Paris. Available at,2546,en_2649_33727_36074136_119666_1_1_1,00.html.
  24. Perron P (1997) Further evidence on breaking trend functions in macroeconomic variables. J Econometrics 80(2): 355–385.CrossRefGoogle Scholar
  25. Ploberger W, Krämer W, Kontrus K (1989) A new test for structural stability in the linear regression model. J Econometrics 40(2): 307–318.CrossRefGoogle Scholar
  26. Rajagopal D, Zilberman D (2007) Review of environmental, economic and policy aspects of biofuel production and use. Policy Research Working paper 4341, The World Bank, Washington DC.Google Scholar
  27. Rajagopal D, Sexton S E, Roland-Holst D, Zilberman D (2007) Challenge of biofuel: filling the tank without emptying the stomach? Environ Res Lett 2(4): 1–9.CrossRefGoogle Scholar
  28. Rapsomanikis G, Hallam D (2006) Threshold cointegration in the sugar-ethanol-oil price system in Brazil: Evidence from nonlinear vector error correction models. FAO Commodity and Trade Policy Research Papers 22, FAO, Rome. Available at
  29. Renewable Fuels Association (2009) Industry Statistics. Available at
  30. Saikkonen P, Luukkonen R (1988) Lagrange multiplier tests for testing non-linearities in time series models. Scandinavian J Stat 15: 55–68.Google Scholar
  31. Schmidhuber J (2006) Impact of an increased biomass use on agricultural markets, prices and food security: A longer-term perspective. Paper presented at the International Symposium of Notre Europe, Paris, 27–29 November.Google Scholar
  32. Shapouri H, Gallagher P (2005) USDA’s 2002 Ethanol Cost-of-Production Survey. Agricultural Economic Report 841, U.S. Department of Agriculture, Washington D.C. Available at
  33. Szklo A, Schaeffer R, Delgado F (2007) Can one say ethanol is a real threat to gasoline? Energy Policy 35(11): 5411–5421.5CrossRefGoogle Scholar
  34. Teräsvirta T (1994) Specification, estimation, and evaluation of smooth transition autoregressive models. J Amer Stat Assoc 89(425): 208–218.CrossRefGoogle Scholar
  35. Tyner W E, Taheripour F (2007) Renewable energy policy alternatives for the future Am J Agri Econ 89(5): 1303–1310.CrossRefGoogle Scholar
  36. United States Department of Agriculture (2008) USDA Agricultural Projections to 2017. Projections Report OCE-2008-1, Washington DC. Available at
  37. van Dijk D, Teräsvirta T, Hans Franses P (2002) Smooth transition autoregressive models – A survey of recent developments. Econom Rev 21(1): 1–47.CrossRefGoogle Scholar
  38. Weise C L (1999) The asymmetric effects of monetary policy: A nonlinear vector autoregression approach. J Money, Credit Banking 31(1): 85–108.CrossRefGoogle Scholar
  39. Westcott P C (2007) Ethanol expansion in the United States. How will the agricultural sector adjust? U.S. Department of Agriculture, Economic Research Service, Washington DC. Available at

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Teresa Serra
    • 1
  • David Zilberman
    • 2
  • José M. Gil
    • 3
  • Barry K. Goodwin
    • 4
  1. 1.Centre de Recerca en Economia i Desenvolupament Agroalimentari (CREDA-UPC-IRTA), Parc Mediterrani de la Tecnologia, Edifici ESABCastelldefelsSpain
  2. 2.Department of Agricultural and Resource EconomicsUniversity of CaliforniaBerkeleyUSA
  3. 3.Centre de Recerca en Economia i Desenvolupament Agroalimentari (CREDA-UPC-IRTA)CastelldefelsSpain
  4. 4.Department of Agricultural and Resource EconomicsNorth Carolina State UniversityRaleighUSA

Personalised recommendations