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Validation of Linear Programming Models

Published online by Cambridge University Press:  05 September 2016

Bruce A. McCarl
Affiliation:
Department of Agricultural Economics, Texas A&M University
Jeffrey Apland
Affiliation:
Department of Agricultural and Applied Economics, University of Minnesota
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Abstract

Systematic approaches to validation of linear programming models are discussed for prescriptive and predictive applications to economic problems. Specific references are made to a general linear programming formulation, however, the approaches are applicable to mathematical programming applications in general. Detailed procedures are outlined for validating various aspects of model performance given complete or partial sets of observed, real world values of variables. Alternative evaluation criteria are presented along with procedures for correcting validation problems.

Type
Submitted Articles
Copyright
Copyright © Southern Agricultural Economics Association 1986

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References

Anderson, J.Simulation: Methodology and Applications in Agricultural Economics.Rev. Marketing and Agr. Econ., 43(1974):355.Google Scholar
Barnett, D., Blake, B., and McCarl, B.. “Goal Programming via Multidimensional Scaling Applied to Senegalese Subsistence Farms.Amer. J. Agr. Econ., 64,4(1982):720727.CrossRefGoogle Scholar
Baumes, H.A Partial Equilibrium Sector Model of U.S. Agriculture Open to Trade: A Domestic Agricultural and Agricultural Trade Policy Analysis.” Ph.D thesis, Purdue University, 1978.Google Scholar
Brink, L. and McCarl, B.. “The Adequacy of a Crop Planning Model for Determining Income, Income Change, and Crop Mix.Can. J. Agr. Econ., 47(1979):1325.CrossRefGoogle Scholar
Day, R. and Cigno, A., editors. Modeling Economic Change: The Recursive Programming Approach. Amsterdam: North-Holland Publishing Co., 1978.Google Scholar
Fajardo, D., McCarl, B., and Thompson, R.. “A Multicommodity Analysis of Trade Policy Effects: The Case of Nicaraguan Agriculture.Amer. J. Agr. Econ., 63,1(1981):2331.CrossRefGoogle Scholar
Garret, H. and Woodworth, R.. Statistics in Psychology and Education. New York: David McKay Co., Inc., 1964.Google Scholar
Gass, S. I.Decision-Aiding Models: Validation, Assessment, and Related Issues for Policy Analysis.Operations Research, 31(1983):603631.CrossRefGoogle Scholar
Hazell, P. and Pomareda, C.. “Evaluating Price Stabilization Schemes with Mathematical Programming.Amer. J. Agr. Econ., 63,3(1981):550556.CrossRefGoogle Scholar
Hazell, P., Norton, R., Parthasarthy, M., and Pomareda, C.. “The Importance of Risk in Agricultural Planning Models.Programming Studies for Mexican Agricultural Policy, Norton, R. and Solis, L. (eds.). New York: Johns Hopkins Press, 1981.Google Scholar
Henderson, J.The Utilization of Agricultural Land: A Theoretical and Empirical Inquiry.Rev. Econ. and Stat., 41(1959):242259.CrossRefGoogle Scholar
Hillier, F. and Lieberman, G.. Introduction to Operations Research. San Francisco: Holden-Day, Inc., 1967.Google Scholar
House, P. and Ball, R., “Validation: A Modern Day Snipe Hunt: Conceptual Difficulties of Validating Models,” in Gass, S.I. (ed.), Validation and Assessment of Issues in Energy Models, Proceedings of a Workshop, National Bureau of Standards, Spec. Pub. 564, Washington, D.C.; 1980.Google Scholar
Johnson, S. and Rausser, G., “Systems Analysis and Simulation: A Survey of Applications in Agricultural and Resource Economics.” in Martin, L. (gen. ed.), A Survey of Agricultural Economies Literature, 2(1977):157301.Google Scholar
Keith, Nancy, “Aggregation in Large-Scale Distribution Systems.” Ph.D. thesis, Purdue University, 1978.Google Scholar
Kutcher, G., “A Regional Agriculture Planning Model for Mexico's Pacific Northwest.” in Norton, R. and Solis, L. (eds.), Programming Studies for Mexican Agricultural Policy, Chapter 11, New York: Johns Hopkins Press, 1983.Google Scholar
Leontief, W., “Quantitative Input-Output Relations in the Economic System of the United States.Rev. Econ. Studies, 18(1936):105125.Google Scholar
Leuthold, Raymond M., “On the Use of Theil's Inequality Coefficients.Amer. J. Agr. Econ., 57, 3(1975):344346.CrossRefGoogle Scholar
McCarl, B.Cropping Activities in Agricultural Sector Models: A Methodological Proposal.Amer. J. Agr. Econ., 64,4(1982):768772.CrossRefGoogle Scholar
McCarl, B. and Spreen, T., “Price Endogenous Mathematical Programming as a Tool for Sector Analysis,Amer. J. Agr. Econ., 62,1(1980):87102.CrossRefGoogle Scholar
Miller, T. and Millar, R., “A Prototype Quadratic Programming Model of the U.S. Food and Fiber System.” mimeo, CES-ERS-USDA and Department of Economics, Colorado State University, 1976.Google Scholar
Nugent, J., “Linear Programming Models for National Planning: Demonstration of a Testing Procedure.Econometrica, 38(1970):831855.CrossRefGoogle Scholar
Paris, Q., “Multiple Optimal Solutions in Linear Programming Models.Amer. J. Agr. Econ., 63, 4(1981):724727.CrossRefGoogle Scholar
Pieri, R., Meilke, K., and MacAuley, T., “North American-Japanese Pork Trade: An Application of Quadratic Programming.Can. J. Agr. Econ., 25(1977):6179.CrossRefGoogle Scholar
Rodriquez, G. and Kunkel, D., “Model Validation and the Philippine Programming Model.Agr. Econ. Res., 32(1980):1725.Google Scholar
Sahi, R. and Craddock, W., “Estimation of Flexibility Coefficients for Recursive Programming Models—Alternative Approaches.Amer. J. Agr. Econ., 56,2(1974):344350.CrossRefGoogle Scholar
Shannon, R., “Simulation: A Survey with Research Suggestions.AHE Transactions, 7(1975):289301.Google Scholar