Skip to main content
SpringerLink
Log in

Diesel and soybean price relationship in the USA: evidence from a quantile autoregressive distributed lag model

  • Published:
Empirical Economics Aims and scope Submit manuscript

Abstract

We use the quantile autoregressive distributed lag model of Galvao et al. (Oxf Bull Econ Stat 75:307–321, 2013) to explore the possible relationship between prices of diesel and soybean. Monthly US diesel and soybean prices spanning from January 2004 to June 2014 are used in this analysis. Strong links between diesel and soybean prices are identified over the long run. Results indicate that soybean price movement is tail-dependent and varies over quantiles. In the upper quantiles, soybean prices respond strongly to diesel price fluctuations as compared to that in the lower quantiles.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Abbott P, Hurt C, Tyner W (2008) What’s driving food prices? Farm foundation issue report. Farm Foundation, Oak Brook

    Google Scholar 

  • Ahti V (2009) Testing the rational expectations competitive storage hypothesis. Helsinki Center of Economic Research Discussion Paper No. 267

  • Bacon RW (1991) Rockets and feathers: the asymmetric speed of adjustment of UK retail gasoline prices to cost changes. Energy Econ 13:211–218

    Article  Google Scholar 

  • Baffes J (2007) Oil spills on other commodities. Resour Policy 32:126–134

    Article  Google Scholar 

  • Barsky R, Delong B (1993) Why does the stock market fluctuate? Q J Econ 108:291–311

    Article  Google Scholar 

  • Campiche J, Bryant H, Richardson J, Outlaw J (2007) Examining the evolving correspondence between petroleum prices and agricultural commodity prices. Paper presented at the American agricultural economics association annual meeting, Portland, OR, 29 July–1August

  • Chang TH, Su HM (2010) The substitutive effect of biofuels on fossil fuels in the lower and higher crude oil price periods. Energy 35:2807–2013

    Article  Google Scholar 

  • Cha KS, Bae JH (2011) Dynamic impacts of high oil prices on the bioethanol and feedstock markets. Energy Policy 39:753–360

    Article  Google Scholar 

  • Chen S, Kuo H, Chen C (2010) Modeling the relationship between the oil price and global food prices. Appl Energy 87:2517–2525

    Article  Google Scholar 

  • Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 74:427–431

    Google Scholar 

  • Donaldson R, Kamstra M (1996) A new dividend forecasting procedure that rejects bubbles in asset prices: the case of 1929’s stock crash. Rev Financ Stud 9:333–383

    Article  Google Scholar 

  • Galvao AF (2009) Unit root quantile autoregression testing using covariates. J Econom 152:165–178

    Article  Google Scholar 

  • Galvao AF Jr, Montes-Rojas G, Park SY (2013) Quantile autoregressive distributed lag model with an application to house price returns. Oxf Bull Econ Stat 75:307–321

    Article  Google Scholar 

  • Gilbert CL (2010) How to understand high food prices. J Agric Econ 61:398–425

    Article  Google Scholar 

  • Hanson K, Robinson S, Schluter G (1993) Sectoral effects of a world oil price shock: economy wide linkages to the agricultural sector. J Agric Resour Econ 18:96–116

    Google Scholar 

  • Hassouneh I, Serra T, Goodwin BK, Gil JM (2012) Non-parametric and parametric modeling of biodiesel, sunflower oil, and crude oil price relationships. Energy Econ 34:1507–1513

    Article  Google Scholar 

  • Johansen S (1988) Statistical-analysis of cointegration vectors. J Econ Dyn Control 12:231–254

    Article  Google Scholar 

  • Johansen S, Juselius K (1990) Maximum likelihood estimation and inference on cointegration—with applications to the demand for money. Oxf Bull Econ Stat 52:169–210

    Article  Google Scholar 

  • Johansen S (1991) Estimation and hypothesis-testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica 59:1551–1580

    Article  Google Scholar 

  • Kammen DL, Ahring MB, Almeida E, Steenblik D, Woods J (2007) Energy and greenhouse gas impacts of biofuels: a framework of analysis. Paris: OECD, International Transport Forum: Joint Transport Research Forum

  • Koenker R, Bassett G (1978) Regression quantiles. Econometrica 46:33–50

    Article  Google Scholar 

  • Koenker R, Xiao Z (2006) Quantile autoregression. J Am Stat Assoc 101:980–990

    Article  Google Scholar 

  • Kwiatkowski D, Phillips PCB, Schmidt P, Shin Y (1992) Testing the null hypothesis of stationary against the alternative of unit root. J Econom 54:159–178

    Article  Google Scholar 

  • Lee C, Zee J (2011) Revisiting the relationship between spot and futures oil prices: evidence from quantile cointegrating regression. Energy Econ 33:924–935

    Article  Google Scholar 

  • Mackinnon JG, Haug AA, Michelis L (1999) Numerical distribution functions of likelihood ratio tests for cointegration. J Appl Econ 14:563–577

    Article  Google Scholar 

  • Mcphail L (2011) Assessing the impact of US ethanol on fossil fuel markets: a structural VAR approach. Energy Econ 33:1177–1185

    Article  Google Scholar 

  • Meyer J, Cramon-Taubadel S (2004) Asymmetric price transmission: a survey. J Agric Econ 55:581–611

    Article  Google Scholar 

  • Mitchell D (2008) A note on rising food prices. World Bank, Development Prospects Group Policy Research Working Paper No. 4682

  • Myers RJ, Johnson SR, Michael H, Harry B (2014) Long-run and short-run co- movements in energy prices and the prices of agricultural feedstocks for biofuel. Am J Agric Econ 96:1–18

    Article  Google Scholar 

  • Nazlioglu S (2011) World oil and agricultural commodity prices: evidence from nonlinear causality. Energy Policy 39:2935–2943

    Article  Google Scholar 

  • Nazlioglu S, Soytas U (2011) World oil and agricultural commodity prices: evidence from an emerging market. Energy Economics 33:488–496

    Article  Google Scholar 

  • OECD-FAO (2014) Agricultural Outlook 2014–2023 OECD, Paris

  • Phillips PCB, Perron P (1988) Testing for a unit root in time series regression. Biometrika 75:335–346

    Article  Google Scholar 

  • Pindyck RS (2004) Volatility in natural gas and oil markets. J Energy Dev 30:1–19

    Google Scholar 

  • Qiu C, Colson G, Escalante C, Wetzstein M (2012) Considering macroeconomic indicators in the food before fuel nexus. Energy Econ 34:2021–2028

    Article  Google Scholar 

  • Reboredo JC (2012) Do food and oil prices co-move? Energy Policy 49:456–467

    Article  Google Scholar 

  • Saghaian SH (2010) The impact of the oil sector on commodity prices: Correlation or causation? J Agric Appl Econ 42:477–485

    Article  Google Scholar 

  • Si S, Chalfant JA, Lawell CL, Yi F (2015) The effects of China’s biofuel policies on agricultural and ethanol markets. University of California at Davis Working paper

  • Serra T, Zilberman D, Gil JM, Goodwin BK (2011) Nonlinearities in the U.S. corn–ethanol–gasoline price system. Agric Econ 42:35–45

    Article  Google Scholar 

  • Serra T, Zilberman D (2013) Biofuel-related price transmission literature: a review. Energy Econ 37:141–151

    Article  Google Scholar 

  • Tyner WE (2010) The integration of energy and agricultural markets. Agric Econ 4:193–201

    Article  Google Scholar 

  • U.S. Energy Information Administration (2014) Monthly biodiesel production report

  • United States Department of Agriculture (2015) World agriculture demand and supply estimate

  • Wiggins S, Keats S (2009) Grain stocks and price spikes. Overseas Development Institute Research Report

  • Xiao Z (2009) Quantile cointegrating regression. J Econ 150:248–260

    Article  Google Scholar 

  • Zilberman D, Hochman G, Rajagopal D, Sexton S, Timilsina G (2012) The impact of biofuels on commodity food prices: assessment of findings. Am J Agric Econ 95:275–281

    Article  Google Scholar 

  • Zhang Z, Lohr L, Escalante C, Wetzstein M (2009) Ethanol, corn, and soybean price relations in a volatile vehicle-fuels market. Energies 2:320–339

    Article  Google Scholar 

  • Zhang Z, Lohr L, Escalante C, Wetzstein M (2010) Food versus fuel: What do prices tell us? Energy Policy 38:445–451

    Article  Google Scholar 

Download references

Acknowledgments

Authors thank the editor and two journal referees for their valued suggestions that enriched the work substantially.

Author information

Authors and Affiliations

  1. Economics and Business Environment, Indian Institute of Management, Raipur, India

    Debdatta Pal

  2. Finance & Accounts, Indian Institute of Management, Raipur, India

    Subrata K. Mitra

Authors
  1. Debdatta Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Subrata K. Mitra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Debdatta Pal.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pal, D., Mitra, S.K. Diesel and soybean price relationship in the USA: evidence from a quantile autoregressive distributed lag model. Empir Econ 52, 1609–1626 (2017). https://doi.org/10.1007/s00181-016-1114-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00181-016-1114-4

Keywords

JEL Classification

Search

Navigation