Methods of Estimating the Input Coefficients for Linear Programming Models

Subhash Ray

American Journal of Agricultural Economics, 1985, vol. 67, issue 3, 660-665

Abstract: In order to apply the linear programming method to determine the most profitable product mix of a farm, it is often necessary to estimate the input coefficients from sample data. The estimated coefficients must all be nonnegative. Estimates from regression models of input demand equations are not always nonnegative. The sample average of inputs used per acre of various crops provides nonnegative estimates but may suffer from selectivity bias. In this study the methods of minimizing the mean of absolute deviations and of minimizing the maximum absolute deviation are discussed as alternatives to inequality-constrained least squares.

Date: 1985
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://hdl.handle.net/10.2307/1241090 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:ajagec:v:67:y:1985:i:3:p:660-665.

Access Statistics for this article

American Journal of Agricultural Economics is currently edited by Madhu Khanna, Brian E. Roe, James Vercammen and JunJie Wu

More articles in American Journal of Agricultural Economics from Agricultural and Applied Economics Association Contact information at EDIRC.
Bibliographic data for series maintained by Oxford University Press ().