EconPapers    
Economics at your fingertips  
 

Simple Methods of Estimating Certain Nonlinear Functions, With Emphasis on Agricultural Data

Richard H. Day

No 305829, Miscellaneous Publications from United States Department of Agriculture, Economic Research Service

Abstract: Excerpt from the report Introduction: This paper presents two elementary methods for fitting three different nonlinear functions to empirical data by means of simple linear regression. Iterative least squares methods which have been developed for estimating parameters of nonlinear functions sometimes lead to certain difficulties in application. Because this is the case the much simpler methods developed in this handbook are useful tools for application. The relative merits of this approach versus the nonlinear iterative approach are briefly described in the concluding-section. The Spillman, Gompertz, and Pearl-Reed (logistic) functions are considered. The two methods presented for the Pearl-Reed function are already well known and are given first. Then analogous methods are derived for the Spillman and the closely related Gompertz curves; these apparently have not been presented in the literature.

Keywords: Research; Methods/Statistical; Methods (search for similar items in EconPapers)
Pages: 31
Date: 1963-08
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/305829/files/ah256.pdf (application/pdf)

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:ags:uersmp:305829

DOI: 10.22004/ag.econ.305829

Access Statistics for this paper

More papers in Miscellaneous Publications from United States Department of Agriculture, Economic Research Service Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().

 
Page updated 2025-04-03
Handle: RePEc:ags:uersmp:305829