An Exact Hierarchical Algorithm for Determining Aggregate Statistics from Individual Choice Data

Dennis H. Gensch and Joseph A. Svestka
Additional contact information
Dennis H. Gensch: University of Wisconsin-Milwaukee
Joseph A. Svestka: University of Wisconsin-Milwaukee

Management Science, 1979, vol. 25, issue 10, 939-952

Abstract: A review of the literature from many disciplines reveals conceptual agreement that individuals choose among alternatives by comparing the attributes of the alternatives in a sequential process. Yet in almost all the published empirical work the model used is a simultaneous compensatory model such as regression, logit, or probit. The sequential choice modeling approach has been severely retarded by the lack of an algorithm to generate the sample statistics projectable to hold-out samples and populations. This paper attempts to fill this void by presenting a prototype aggregate hierarchical model, called HIARC, for analyzing individual choice decisions. HIARC can be viewed as a semi-order lexicographic model that empirically estimates a set of tolerances directly from the data. HIARC and logit are applied to the same empirical data set. While the predictive accuracy is about equal, the two approaches yield different types of diagnostic information and the set of individuals whose choice is correctly predicted by one method is substantially different from the set correctly predicted by the other method.

Keywords: marketing; marketing: buyer behavior; decision analysis: sequential (search for similar items in EconPapers)
Date: 1979
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.25.10.939 (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:inm:ormnsc:v:25:y:1979:i:10:p:939-952

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().