Title: Partial Stochastic Analysis with the Aglink-Cosimo Model: A Methodological Overview
Authors: ARAUJO ENCISO SERGIO RENÉPIERALLI SIMONEPEREZ DOMINGUEZ IGNACIO
Publisher: Publications Office of the European Union
Publication Year: 2017
JRC N°: JRC108837
ISBN: 978-92-79-76019-8 (print)
978-92-79-76018-1 (pdf)
ISSN: 1018-5593 (print)
1831-9424 (online)
Other Identifiers: EUR 28863 EN
OP KJ-NA-28863-EN-C (print)
OP KJ-NA-28863-EN-N (online)
URI: http://publications.jrc.ec.europa.eu/repository/handle/JRC108837
DOI: 10.2760/450029
10.2760/680976
Type: EUR - Scientific and Technical Research Reports
Abstract: Aglink-Cosimo is a recursive-dynamic partial equilibrium model developed and maintained by the OECD and FAO Secretariats as a collaborative effort. The model is primarily used to prepare the OECD-FAO Agricultural Outlook, a yearly publication aiming at providing baseline projections for the main global agricultural commodities over the medium term. This deterministic projections are enhanced by a Partial Stochastic Analysis tool, which allows for the analysis of specific market uncertainties. This is done by producing counterfactual scenarios to the baseline originating from varying yields and macroeconomic variables stochastically. The aim of this report is to propose and evaluate different methods of analysing stochastically important yields and macroeconomic uncertainty drivers. In a first stage, we identify and evaluate the best parametric method to extract unexplained variability, which we consider as uncertainty in the macro and yield drivers. In a second stage, we test parametric and nonparametric methods side by side to simulate ten years of potentially different macroeconomic and yield environments. The results can be summarised as follows. For yields, we find out that a parametric cubic trend method performs best in the first stage and a non-parametric hierarchical copula (Clayton) method is more appropriate in the second stage. For macroeconomic variables, a vector autoregressive model performs best in the first stage, while a non-parametric hierarchical copula (Frank) method is more appropriate in the second stage.
JRC Directorate:Sustainable Resources

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