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Estimation of Dynamic Nonlinear Rational Expectations Models of Primary Commodity Markets with Private and Government Stockholding

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  • Miranda, Mario J
  • Glauber, Joseph W

Abstract

Stochastic-dynamic programming and disequilibrium maximum likelihood methods are combined to estimate a dynamic nonlinear rational expectations model of a market for a storable primary commodity. The estimation model captures the inherently nonlinear structure of private stockholding dynamics, the disequilibrium effects of government buffer stock intervention, and the impact of price expectations and risk on private supply and stockholding decisions. Copyright 1993 by MIT Press.

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  • Miranda, Mario J & Glauber, Joseph W, 1993. "Estimation of Dynamic Nonlinear Rational Expectations Models of Primary Commodity Markets with Private and Government Stockholding," The Review of Economics and Statistics, MIT Press, vol. 75(3), pages 463-470, August.
    • Handle: RePEc:tpr:restat:v:75:y:1993:i:3:p:463-70
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    4. Cesar Revoredo, 2000. "On The Solution Of The Dynamic Rational Expectations Commodity Storage Model In The Presence Of Stockholding By Speculators And Processors," Computing in Economics and Finance 2000 42, Society for Computational Economics.
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    9. Wang, Dabin & Tomek, William G., 2005. "Characterizing Distributions of Class III Milk Prices: Implications for Risk Management," Working Papers 127085, Cornell University, Department of Applied Economics and Management.
    10. Miranda, Mario J. & Rui, Xiongwen, 1997. "Maximum likelihood estimation of the nonlinear rational expectations asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 21(8-9), pages 1493-1510, June.
    11. Peterson, Hikaru Hanawa & Tomek, William G., 2000. "Commodity Price Behavior: A Rational Expectations Storage Model of Corn," Working Papers 127682, Cornell University, Department of Applied Economics and Management.
    12. Tokovenko, Oleksiy & Gunter, Lewell F., 2008. "Quarterly Storage Model of U.S. Cotton Market: Estimation of the Basis under Rational Expectations," 2008 Annual Meeting, July 27-29, 2008, Orlando, Florida 6435, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
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    16. Hikaru Hanawa Peterson & William G. Tomek, 2005. "How much of commodity price behavior can a rational expectations storage model explain?," Agricultural Economics, International Association of Agricultural Economists, vol. 33(3), pages 289-303, November.
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    18. Makki, Shiva S. & Tweeten, Luther G. & Miranda, Mario J., 2001. "Storage-trade interactions under uncertainty: Implications for food security," Journal of Policy Modeling, Elsevier, vol. 23(2), pages 127-140, February.
    19. Ye, Michael & Zyren, John & Shore, Joanne, 2006. "Forecasting short-run crude oil price using high- and low-inventory variables," Energy Policy, Elsevier, vol. 34(17), pages 2736-2743, November.
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    21. Michael Ye & John Zyren & Joanne Shore, 2002. "Forecasting crude oil spot price using OECD petroleum inventory levels," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 8(4), pages 324-333, November.
    22. Vorotnikova, Ekaterina, 2016. "Optimal Storage Capacity Allocation in Grain Merchandizing," 2016 Annual Meeting, February 6-9, 2016, San Antonio, Texas 230128, Southern Agricultural Economics Association.
    23. Berg, Ernst, 2017. "Impacts of Inventory Management on Price Volatility in Agricultural Commodity Markets: Insights from a System Dynamics Model," 2017 International Congress, August 28-September 1, 2017, Parma, Italy 261281, European Association of Agricultural Economists.

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