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Dynamics of World Commodity Prices: A Microsimulation Model

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  • Jan Bruha
  • Vitezslav Pisa

Abstract

The ability to explain the development of supply, demand, and prices of commodities (especially agricultural and energy commodities) is essential for policy-makers around the globe for several reasons since they represent an essential part of the consumption baskets. Moreover, energy and agricultural products are associated with several highly actual topics: insecure supply of fossil energy products associated with high crude oil prices, increasing supply of first generation biofuels associated with starvation in selected developing countries, or land use changes linked to growing energy demand and substitution of conventional energy products by their bio-alternatives. This paper presents a multiregional microsimulation model to address the various effects influencing real prices and quantities of energy and agricultural products. The model is characterized by supply and demand block for both energy and agricultural commodities and stems from the decomposition model of world agricultural and energy production and consumption. The demand for food per capita is a function of the world composition of the real GDP and real food prices. The supply of food is a function of labor, capital, and agricultural land. The parameters of the models and missing data are treated jointly via Bayesian methods (MCMC algorithms). Based on assumptions about the growth in real GDP and population in chosen regions, we simulate impacts on real agricultural prices and quantities.

Suggested Citation

  • Jan Bruha & Vitezslav Pisa, 2012. "Dynamics of World Commodity Prices: A Microsimulation Model," EcoMod2012 4182, EcoMod.
  • Handle: RePEc:ekd:002672:4182
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    References listed on IDEAS

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    1. Ang, B. W., 2004. "Decomposition analysis for policymaking in energy:: which is the preferred method?," Energy Policy, Elsevier, vol. 32(9), pages 1131-1139, June.
    2. B. W. Ang & Ki-Hong Choi, 1997. "Decomposition of Aggregate Energy and Gas Emission Intensities for Industry: A Refined Divisia Index Method," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 59-73.
    3. Hoekstra, Rutger & van den Bergh, Jeroen C. J. M., 2003. "Comparing structural decomposition analysis and index," Energy Economics, Elsevier, vol. 25(1), pages 39-64, January.
    4. Stern, David I., 2002. "Explaining changes in global sulfur emissions: an econometric decomposition approach," Ecological Economics, Elsevier, vol. 42(1-2), pages 201-220, August.
    5. Ang, B.W. & Liu, F.L. & Chung, Hyun-Sik, 2004. "A generalized Fisher index approach to energy decomposition analysis," Energy Economics, Elsevier, vol. 26(5), pages 757-763, September.
    6. Ang, B.W. & Liu, F.L., 2001. "A new energy decomposition method: perfect in decomposition and consistent in aggregation," Energy, Elsevier, vol. 26(6), pages 537-548.
    7. Jeroen C.J.M. van den Bergh (ed.), 1999. "Handbook of Environmental and Resource Economics," Books, Edward Elgar Publishing, number 801.
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