Food versus Fuel: Causality and Predictability in Distribution
Andrea Bastianin, Marzio Galeotti, Matteo Manera
C22, C53, Q13, Q42, Q47
Biofuels, Ethanol, Field Crops, Density Forecasting, Granger Causality, Quantiles
Energy: Resources and Markets
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, Causality and predictability in distribution: The ethanol–food price relation revisited, Energy Economics, Volume 42, March 2014, Pages 152-160, ISSN 0140-9883, http://dx.doi.org/10.1016/j.eneco.2013.12.014