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Reduction Of State Variable Dimension In Stochastic Dynamic Optimization Models Which Use Time-Series Data

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  • Burt, Oscar R.
  • Taylor, C. Robert
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    Abstract

    Statistical procedures are developed for reducing the number of autonomous state variables in stochastic dynamic optimization models when these variables follow a stationary process over time. These methods essentially delete part of the information upon which decisions are based while maintaining a logically consistent model. The relatively simple linear autoregressive process as well as the general case is analyzed and the necessary formulae for practical application are derived. Several applications in agricultural economics are discussed and results presented which quantify the relative amount of information sacrificed with the reduction in number of state variables.

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    File URL: http://purl.umn.edu/32349
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    Bibliographic Info

    Article provided by Western Agricultural Economics Association in its journal Western Journal of Agricultural Economics.

    Volume (Year): 14 (1989)
    Issue (Month): 02 (December)
    Pages:

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    Handle: RePEc:ags:wjagec:32349

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    Web page: http://waeaonline.org/
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    Related research

    Keywords: Research Methods/ Statistical Methods;

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    Cited by:
    1. King, Robert P. & Lohano, Heman D., 2006. "Accuracy of Numerical Solution to Dynamic Programming Models," Staff Papers 14230, University of Minnesota, Department of Applied Economics.
    2. Tronstad, Russell, 1997. "Value of Pregnancy Testing Range Cows," 1997 Annual Meeting, July 13-16, 1997, Reno\Sparks, Nevada 35771, Western Agricultural Economics Association.

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