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Estimating the Technology Coefficients in Linear Programming Models

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  • Dixon, Bruce L.
  • Hornbaker, Robert H.

Abstract

Linear constraints for mathematical programming models are . demonstrated to be random coefficient regression (RCR) models when estimating constraint coefficients from samples. Monte·carlo experiments show an RCR estimator preferable to least squares although least squares is also acceptable. Dependence between output levels and technical coefficients can lead to biased estimates.

Suggested Citation

  • Dixon, Bruce L. & Hornbaker, Robert H., 1989. "Estimating the Technology Coefficients in Linear Programming Models," 1989 Annual Meeting, July 30-August 2, Baton Rouge, Louisiana 270519, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    • Handle: RePEc:ags:aaea89:270519
    DOI: 10.22004/ag.econ.270519
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    References listed on IDEAS

    as
    1. Subhash C. Ray, 1985. "Methods of Estimating the Input Coefficients for Linear Programming Models," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 67(3), pages 660-665.
    2. Havenner, A. & Swamy, P. A. V. B., 1981. "A random coefficient approach to seasonal adjustment of economic time series," Journal of Econometrics, Elsevier, vol. 15(2), pages 177-209, February.
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