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Verification of Linear Programming Solutions, with Emphasis on Supply Implications

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  • Shumway, C. Richard
  • Talpaz, Hovav

Abstract

Linear programming (LP) models have been developed for a wide range of normative purposes in agricultural production economics. Despite their widespread application, a pervading concern among users is reliability — how well does a particular model actually describe and/or predict real world phenomena when it is so designed. Much attention has been devoted in recent years to methods for making programming models produce results more in line with those actually observed. These efforts have included development of more detail in production activities and restrictions, incorporation of flexibility constraints into recursive programming systems, specification of more realistic behavioral properties, and development of guidelines for reducing aggregation error.

Suggested Citation

  • Shumway, C. Richard & Talpaz, Hovav, 1977. "Verification of Linear Programming Solutions, with Emphasis on Supply Implications," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 9(2), pages 1-8, December.
    • Handle: RePEc:cup:jagaec:v:9:y:1977:i:02:p:1-8_01
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    References listed on IDEAS

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    1. Quirino Paris & Gordon C. Rausser, 1973. "Sufficient Conditions for Aggregation of Linear Programming Models," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 55(4_Part_1), pages 659-666.
    2. Marc Nerlove, 1956. "Estimates of the Elasticities of Supply of Selected Agricultural Commodities," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 38(2), pages 496-509.
    3. Debertin, David L. & Freund, R. J., 1975. "The Deletion of Variables From Regression Models Based on Tests of Significance: A Statistical and Moral Issue," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 7(1), pages 211-216, July.
    4. Cooper, J Phillip, 1972. "Asymptotic Covariance Matrix of Procedures for Linear Regression in the Presence of First- Order Autoregressive Disturbances," Econometrica, Econometric Society, vol. 40(2), pages 305-310, March.
    5. Schaller, W. N. & Dean, G. W., 1965. "Predicting Regional Crop Production," Technical Bulletins 171259, United States Department of Agriculture, Economic Research Service.
    6. C. Richard Shumway & Anne A. Chang, 1977. "Linear Programming versus Positively Estimated Supply Functions: An Empirical and Methodological Critique," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 59(2), pages 344-357.
    7. Talpaz, Hovav, 1976. "Nonlinear Estimation by an Efficient Numerical Search Method," The Review of Economics and Statistics, MIT Press, vol. 58(4), pages 501-504, November.
    8. Jean-Marc Boussard, 1971. "Time Horizon, Objective Function, and Uncertainty in a Multiperiod Model of Firm Growth," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 53(3), pages 467-477.
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