IDEAS home Printed from https://ideas.repec.org/p/ekd/002672/4182.html
   My bibliography  Save this paper

Dynamics of World Commodity Prices: A Microsimulation Model

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://ecomod.net/system/files/Bruha_Pisa_v03.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Miao & Feng, Chao, 2017. "Analysis of energy-related CO2 emissions in China’s mining industry: Evidence and policy implications," Resources Policy, Elsevier, vol. 53(C), pages 77-87.
    2. Wang, H. & Ang, B.W. & Su, Bin, 2017. "Assessing drivers of economy-wide energy use and emissions: IDA versus SDA," Energy Policy, Elsevier, vol. 107(C), pages 585-599.
    3. Su, Bin & Ang, B.W., 2012. "Structural decomposition analysis applied to energy and emissions: Some methodological developments," Energy Economics, Elsevier, vol. 34(1), pages 177-188.
    4. Chen, Jiandong & Xu, Chong & Cui, Lianbiao & Huang, Shuo & Song, Malin, 2019. "Driving factors of CO2 emissions and inequality characteristics in China: A combined decomposition approach," Energy Economics, Elsevier, vol. 78(C), pages 589-597.
    5. Lin, Boqiang & Du, Kerui, 2014. "Decomposing energy intensity change: A combination of index decomposition analysis and production-theoretical decomposition analysis," Applied Energy, Elsevier, vol. 129(C), pages 158-165.
    6. Lenzen, Manfred, 2006. "Decomposition analysis and the mean-rate-of-change index," Applied Energy, Elsevier, vol. 83(3), pages 185-198, March.
    7. Lizhan Cao & Hui Wang, 2022. "The Slowdown in China’s Energy Consumption Growth in the “New Normal” Stage: From Both National and Regional Perspectives," Sustainability, MDPI, vol. 14(7), pages 1-21, April.
    8. Zhao, Xiaoli & Li, Na & Ma, Chunbo, 2012. "Residential energy consumption in urban China: A decomposition analysis," Energy Policy, Elsevier, vol. 41(C), pages 644-653.
    9. Banie Naser Outchiri, 2020. "Contributing to better energy and environmental analyses: how accurate are decomposition analysis results?," Cahiers de recherche 20-11, Departement d'économique de l'École de gestion à l'Université de Sherbrooke.
    10. Wang, Wenchao & Mu, Hailin & Kang, Xudong & Song, Rongchen & Ning, Yadong, 2010. "Changes in industrial electricity consumption in china from 1998 to 2007," Energy Policy, Elsevier, vol. 38(7), pages 3684-3690, July.
    11. Wang, Miao & Feng, Chao, 2018. "Decomposing the change in energy consumption in China's nonferrous metal industry: An empirical analysis based on the LMDI method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2652-2663.
    12. Román, Rocío & Cansino, José M. & Rodas, José A., 2018. "Analysis of the main drivers of CO2 emissions changes in Colombia (1990–2012) and its political implications," Renewable Energy, Elsevier, vol. 116(PA), pages 402-411.
    13. Ang, B.W. & Liu, Na, 2007. "Handling zero values in the logarithmic mean Divisia index decomposition approach," Energy Policy, Elsevier, vol. 35(1), pages 238-246, January.
    14. Cahill, Caiman J. & Ó Gallachóir, Brian P., 2010. "Monitoring energy efficiency trends in European industry: Which top-down method should be used?," Energy Policy, Elsevier, vol. 38(11), pages 6910-6918, November.
    15. Román-Collado, Rocío & Cansino, José M. & Botia, Camilo, 2018. "How far is Colombia from decoupling? Two-level decomposition analysis of energy consumption changes," Energy, Elsevier, vol. 148(C), pages 687-700.
    16. Lan, Jun & Malik, Arunima & Lenzen, Manfred & McBain, Darian & Kanemoto, Keiichiro, 2016. "A structural decomposition analysis of global energy footprints," Applied Energy, Elsevier, vol. 163(C), pages 436-451.
    17. Cahill, Caiman J. & Ó Gallachóir, Brian P., 2012. "Combining physical and economic output data to analyse energy and CO2 emissions trends in industry," Energy Policy, Elsevier, vol. 49(C), pages 422-429.
    18. Zhao, Xiaoli & Ma, Chunbo & Hong, Dongyue, 2010. "Why did China's energy intensity increase during 1998-2006: Decomposition and policy analysis," Energy Policy, Elsevier, vol. 38(3), pages 1379-1388, March.
    19. Yang Yu & Qiuyue Kong, 2017. "Analysis on the influencing factors of carbon emissions from energy consumption in China based on LMDI method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 88(3), pages 1691-1707, September.
    20. Carlino, Laurent & Coppens, François & González, Javier & Ortega, Manuel & Pérez-Duarte, Sébastien & Rubbrecht, Ilse & Vennix, Saskia, 2017. "Decomposition techniques for financial ratios of European non-financial listed groups," Statistics Paper Series 21, European Central Bank.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ekd:002672:4182. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Theresa Leary (email available below). General contact details of provider: https://edirc.repec.org/data/ecomoea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.