Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-25T14:36:45.387Z Has data issue: false hasContentIssue false
  • English
  • Français

Verification of Linear Programming Solutions, with Emphasis on Supply Implications*

Published online by Cambridge University Press:  28 April 2015

C. Richard Shumway  and
Hovav Talpaz
C. Richard Shumway
Affiliation:
Texas A & M University
Get access Rights & Permissions [Opens in a new window]

Extract

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.

Type
Research Article
Information
Journal of Agricultural and Applied Economics , Volume 9 , Issue 2 , December 1977 , pp. 1 - 8
Copyright
Copyright © Southern Agricultural Economics Association 1977

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

Technical Article 12738 of the Texas Agricultural Experiment Station. This paper is a revised and expanded version of “Combining LP Results and Time Series Data for Prediction of Supply: Two Approaches,” contributed paper presented at AAEA annual meeting. State College, Pennsylvania, 15-18 August, 1976. The authors wish to thank Peter Barry, John Penson, C. Robert Taylor and anonymous Journal reviewers for constructive comments on earlier drafts. Anne Chang provided much computer assistance for which we are most appreciative.

References

[1] Boussard, J. M. “Time Horizon, Objective Function, and Uncertainty in a Multiperiod Model of Firm Growth,” American Journal of Agricultural Economics, Volume 53, 1971, pp. 467–77.CrossRefGoogle Scholar
[2[ California Crop and Livestock Reporting Service. Annual Field Crop Summary, Sacramento, 1976.Google Scholar
[3[ California Crop and Livestock Reporting Service. Annual Vegetable Summary, Sacramento, 1975, 1976.Google Scholar
[4[ California Crop and Livestock Reporting Service. California Field Crop Statistics, Sacramento, 1958, 1965, 1972, 1973, 1975.Google Scholar
[5[ California Crop and Livestock Reporting Service. California Vegetable Crops, Sacramento, 1953, 1955, 1962, 1968, 1973, 1974.Google Scholar
[6[ Cochrane, D. and Orcutt, G. H.. “Application of Least Squares Regression to Relationships Containing Autocorrelated Error Terms,” Journal of the American Statistics Association, Volume 44, 1949, pp. 32–61.Google Scholar
[7[ Cooper, J. P. “Asymptotic Covariance Matrix of Procedures of Linear Regression in the Presence of First-Order Autoregressive Disturbances,” Econometrica, Volume 40, 1972, pp. 305–10.Google Scholar
[8[ Debertin, D. L. and Freund, R. J.. “The Deletion of Variables from Regression Models Based on Tests of Significance: A Statistical and Moral Issue,” Southern Journal of Agricultural Economics, Volume VII, No. 1, 1975, pp. 211–15.Google Scholar
[9[ Nerlove, Marc. “Estimates of the Elasticities of Supply of Selected Agricultural Commodities,” Journal of Farm Economics, Volume 38, 1956, pp. 496–509.CrossRefGoogle Scholar
[10[ Perrin, R. K. “Linear Programming in Rural Development Research,” in Quantitative Techniques with Application to Rural Development Research, (Ed., Bradford, G. L. and Saunders, F. B.), Southern Farm Management Research Committee Conference, 1972.Google Scholar
[11[ Rausser, G. C. and Paris, Q.. “Sufficient Conditions for Aggregation of Linear Programming Models,” American Journal of Agricultural Economics, Volume 55, 1973, pp. 659-66.Google Scholar
[12[ Schaller, W.N. and Dean, G. W.. Predicting Regional Crop Production: An Application of Recursive Programming, USDA ERS Technical Bulletin 1329, April 1965.Google Scholar
[13[ Shumway, C. R. and Chang, A. A.. “Linear Programming vs. Positively Estimated Supply Functions—An Empirical and Methodological Critique,” American Journal of Agricultural Economics, Volume 59, 1977, pp. 344–57.CrossRefGoogle Scholar
[14[ Shumway, C. R., King, G. A., Carter, H. O. and Dean, G. W.. Regional Resource Use for Agricultural Production in California, 1961-65 and 1980, Gianinni Foundation Monograph-No. 25, University of California, September 1970.Google Scholar
[15[ Talpaz, Hovav. “Nonlinear Estimation by an Efficient Numerical Search Method,” The Review of Economics and Statistics, Volume 58, 1976, pp. 501–04.CrossRefGoogle Scholar
[16[ U.S. Department of Agriculture. Agricultural Statistics, 1973.Google Scholar
[17[ U.S. Department of Agriculture. Farm Index, Volume 13, No. 4, 1974, p. 23.Google Scholar
[18[ Wallace, T. D. “The General Problem of Spatial Equilibrium: A Methodological Issue,” Interregional Competition Research Methods, Ed. King, R. A., pp. 11–17, North Carolina State University, Agricultural Policy Institute Series 10, 1964.Google Scholar