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Food versus Fuel: Causality and Predictability in Distribution

Marzio Galeotti (), Andrea Bastianin () and Matteo Manera ()

Departmental Working Papers from Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano

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.

Keywords: Biofuels; Ethanol; Field Crops; Density Forecasting; Granger Causality; Quantiles (search for similar items in EconPapers)
JEL-codes: C22 C53 Q13 Q42 Q47 (search for similar items in EconPapers)
Date: 2013-06-29
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Related works:
Journal Article: Causality and predictability in distribution: The ethanol–food price relation revisited (2014) Downloads
Working Paper: Food versus Fuel: Causality and Predictability in Distribution (2013) Downloads
Working Paper: Food versus Fuel: Causality and Predictability in Distribution (2013) Downloads
Working Paper: Food versus Fuel: Causality and Predictability in Distribution (2013) Downloads
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