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Some Illustrations Of Chaos In Commodity Models

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  • M. Burton

Abstract

This paper develops three commodity models which exhibit chaotic behaviour. The examples chosen are a demand system and two cobweb supply and demand models. The latter differ from the standard forms in that they include risk aversion and a new specification for the formation of price expectations. Simulation of the model highlights three implications of chaos: such systems generate complex time‐paths even if the exogenous variables within the model are held constant; the simulated time path is critically sensitive to the starting value of variables, and parameter values; and the average behaviour of the system is critically sensitive to parameter and exogenous variable values. ‘Critically sensitive’ means that very small changes in parameter or starting values leads to substantial changes in the time paths of the variables in the model. These results suggest that if real commodity sectors can be characterised as chaotic systems, then the ability to conduct forecasting and policy analysis of such sectors will be severely curtailed.

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  • M. Burton, 1993. "Some Illustrations Of Chaos In Commodity Models," Journal of Agricultural Economics, Wiley Blackwell, vol. 44(1), pages 38-50, January.
    • Handle: RePEc:bla:jageco:v:44:y:1993:i:1:p:38-50
    DOI: 10.1111/j.1477-9552.1993.tb00249.x
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    Cited by:

    1. Walter Labys, 2005. "Commodity Price Fluctuations: A Century of Analysis," Working Papers Working Paper 2005-01, Regional Research Institute, West Virginia University.
    2. Chiarella, Carl & He, Xue-Zhong & Hung, Hing & Zhu, Peiyuan, 2006. "An analysis of the cobweb model with boundedly rational heterogeneous producers," Journal of Economic Behavior & Organization, Elsevier, vol. 61(4), pages 750-768, December.
    3. Labys, W C & Lesourd, J B & Badillo, D, 1998. "The existence of metal price cycles," Resources Policy, Elsevier, vol. 24(3), pages 147-155, September.
    4. Jin, Hyun Joung & Frechette, Darren L., 2002. "Fractal Geometry In Agricultural Cash Price Dynamics," 2002 Annual meeting, July 28-31, Long Beach, CA 19696, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    5. Bacsi, Zsuzsanna, 1997. "Modelling chaotic behaviour in agricultural prices using a discrete deterministic nonlinear price model," Agricultural Systems, Elsevier, vol. 55(3), pages 445-459, November.
    6. Bunyamin Demir & Nesrin Alptekin & Yilmaz Kilicaslan & Mehmet Ergen & Nilgun Caglairmak Uslu, 2015. "Forecasting Agricultural Production: A Chaotic Dynamic Approach," World Journal of Applied Economics, WERI-World Economic Research Institute, vol. 1(1), pages 65-80, June.
    7. Carl Chiarella & Xue-Zhong He & Peiyuan Zhu, 2003. "Fading Memory Learning in the Cobweb Model with Risk Averse Heterogeneous Producers," Research Paper Series 108, Quantitative Finance Research Centre, University of Technology, Sydney.
    8. Anning Wei & Raymond M. Leuthold, 1998. "Long Agricultural Futures Prices: ARCH, Long Memory, or Chaos Processes?," Finance 9805001, University Library of Munich, Germany.
    9. repec:rri:wpaper:200501 is not listed on IDEAS
    10. Boussard, Jean-Marc, 1996. "When risk generates chaos," Journal of Economic Behavior & Organization, Elsevier, vol. 29(3), pages 433-446, May.

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