Food versus Fuel: Causality and Predictability in Distribution
AbstractThis 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.
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Bibliographic InfoPaper provided by Fondazione Eni Enrico Mattei in its series Working Papers with number 2013.23.
Date of creation: Mar 2013
Date of revision:
Biofuels; Ethanol; Field Crops; Density Forecasting; Granger Causality; Quantiles;
Other versions of this item:
- Marzio GALEOTTI & Andrea BASTIANIN & Matteo MANERA, 2013. "Food versus Fuel: Causality and Predictability in Distribution," Departmental Working Papers 2013-10, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
- 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.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull 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
This paper has been announced in the following NEP Reports:
- NEP-AGR-2013-04-06 (Agricultural Economics)
- NEP-ALL-2013-04-06 (All new papers)
- NEP-FOR-2013-04-06 (Forecasting)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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