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Food versus Fuel: Causality and Predictability in Distribution

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  • Andrea Bastianin
  • Marzio Galeotti
  • Matteo Manera

Abstract

This paper examines the relationship between biofuels and commodity food prices in the U.S. from a new perspective. While a large body of literature has tried to explain the linkages between sample means and volatilities associated with ethanol and agricultural price returns, little is known about their whole distributions. We focus on predictability in distribution by asking whether ethanol returns can be used to forecast different parts of field crops returns distribution, or vice versa. Density forecasts are constructed using Conditional Autoregressive Expectile models estimated with Asymmetric Least Squares. Forecast evaluation relies on quantile-weighed scoring rules, which identify regions of the distribution of interest to the analyst. Results show that both the centre and the left tail of the ethanol returns distribution can be predicted by using field crops returns. On the contrary, there is no evidence that ethanol can be used to forecast any region of the field crops distribution.

Suggested Citation

  • Andrea Bastianin & Marzio Galeotti & Matteo Manera, 2013. "Food versus Fuel: Causality and Predictability in Distribution," Working Papers 241, University of Milano-Bicocca, Department of Economics, revised Mar 2013.
  • Handle: RePEc:mib:wpaper:241
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    Citations

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    Cited by:

    1. da Silveira, Rodrigo Lanna F. & Mattos, Fabio L., 2015. "Price And Volatility Transmission In Livestock And Grain Markets: Examining The Effect Of Increasing Ethanol Production Across Countries," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205684, Agricultural and Applied Economics Association;Western Agricultural Economics Association.
    2. Bastianin, Andrea & Galeotti, Marzio & Manera, Matteo, 2016. "Ethanol and field crops: Is there a price connection?," Food Policy, Elsevier, vol. 63(C), pages 53-61.
    3. Ahmadi, Maryam & Bashiri Behmiri, Niaz & Manera, Matteo, 2016. "How is volatility in commodity markets linked to oil price shocks?," Energy Economics, Elsevier, vol. 59(C), pages 11-23.
    4. Troster, Victor & Shahbaz, Muhammad & Uddin, Gazi Salah, 2018. "Renewable energy, oil prices, and economic activity: A Granger-causality in quantiles analysis," Energy Economics, Elsevier, vol. 70(C), pages 440-452.
    5. Andrea Bastianin & Marzio Galeotti & Matteo Manera, 2011. "Forecast Evaluation in Call Centers: Combined Forecasts, Flexible Loss Functions and Economic Criteria," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1109, Universitá degli Studi di Milano.
    6. Ladislav Kristoufek & Karel Janda & David Zilberman, 2015. "Co-movements of Ethanol Related Prices: Evidence from Brazil and the USA," CAMA Working Papers 2015-11, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    7. Janda, Karel & Krska, Stepan & Prusa, Jan, 2014. "Odhad nákladů na podporu české fotovoltaické energie
      [The Estimation of the Cost of Promotion of the Czech Photovoltaic Energy]
      ," MPRA Paper 54108, University Library of Munich, Germany.

    More about this item

    Keywords

    Biofuels; Ethanol; Field Crops; Density Forecasting; Granger Causality; Quantiles;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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