Demand Estimation For Agricultural Processing Co-Products
AbstractCo-products of processing agricultural commodities are often marketed through private transaction rather than through public markets or those in which public transaction information is recorded or available. The resulting lack of historical price information prohibits the use of positive time series techniques to estimate demand. Demand estimates for co-products are of value to both livestock producers, who obtain them for use in livestock rations, and processors, who must sell or otherwise dispose of them. Linear programming has long been used, first by researchers and later as a mainstream tool for nutritionists and producers, to formulate least cost livestock rations. Here it is used as a normative technique to estimate step function demand schedules for co-products by individual livestock classes within a region. Regression is then used to smooth step function demand schedules by fitting demand data to generalized Leontief cost functions. Seemingly unrelated regression is used to estimate factor demand first adjusted for data censoring using probit analysis. Demand by individual livestock classes is aggregated over the number of livestock within a region. Species important to demand for each co-product are identified and own price elasticity for individual livestock classes and all livestock are estimated.
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Bibliographic InfoPaper provided by Western Agricultural Economics Association in its series 2001 Annual Meeting, July 8-11, 2001, Logan, Utah with number 36172.
Date of creation: 2001
Date of revision:
Agribusiness; Demand and Price Analysis;
Other versions of this item:
- Wachenheim, Cheryl J. & Novak, Patrick J. & DeVuyst, Eric A. & Lambert, David K., 2001. "Demand Estimation For Agricultural Processing Co-Products," Agribusiness & Applied Economics Report 23488, North Dakota State University, Department of Agribusiness and Applied Economics.
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- J. Scott Shonkwiler & Steven T. Yen, 1999. "Two-Step Estimation of a Censored System of Equations," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(4), pages 972-982.
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