No. 06 - Modeling commodity markets in stochastic contexts: A practical guide using the RECS toolbox version 0.5

Christophe Gouel

Variants of rational expectations storage models are central to neoclassical studies of the behavior of markets for storable commodities (Williams and Wright, 1991, Wright, 2001). Simple versions are tested econometrically, for example in Deaton and Laroque (1992) and Cafiero et al. (2011). More complex models are used to analyze the effects of public interventions in commodity markets (Miranda and Helmberger, 1988, Gouel and Jean, 2012).

Like most dynamic stochastic problems, this model cannot be solved analytically. But contrary to most stochastic problems studied, it presents specific numerical difficulties related to the non-negativity constraint on storage. This feature prevents this model from being solved with popular software, such as Dynare, which rely on perturbation methods and cannot handle occasionally binding constraints.

The lack of user-friendly softwares to solve storage models in the past may have represented a serious barrier to entry for research on these issues. The RECS toolbox provides a modeling environment allowing economists to focus on the economic problem at hand, while abstracting from various issues related to the numerical implementation.

This document describes the RECS toolbox and also several applications of this modeling framework to commodity markets related issues. It assumes basic knowledge of Matlab, and of dynamic economic models (see Adda and Cooper, 2003, for an introduction). Storage models are presented in brief; for more information please refer to the original papers or to Williams and Wright (1991), which provide detailed descriptions of many of these models.

*This tutorial is supported by the AGRODEP project.

You can find more information about the RECS model here.


Publication date
Source / Citation
Gouel, C. 2013. Modeling commodity markets in stochastic contexts: A practical guide using the RECS toolbox version 0.5. AGRODEP Technical Note 06. Washington, DC: International Food Policy Research Institute.