Paper Info
Title
A Bayesian approach to the spatial representation of market structure from consumer choice data
Abstract
This paper is concerned with the spatial representation of market structure calibrated on actual or intended choice data. Previous models developed for that purpose accommodate consumer heterogeneity by estimating parameters for each consumer, typically using the method of maximum likelihood. This approach to heterogeneity avoids assuming any particular distribution of the individual level parameters across the sample, but leads to problems related to the consistency of the estimates, sufficient degrees of freedom, and the validity of asymptotic standard errors and test statistics. Of greater concern is the assumption of independence of the choice observations within the same individual. This assumption is necessary in a maximum likelihood (MLE) framework to make the estimation computationally feasible. However, the marketing and psychology literature (cf. Manrai, 1995; Tversky and Simonson, 1993; Kim et al., forth coming) demonstrates that dependencies among choice alternatives may exist, and negligence to take such covariance into account may lead to inconsistent estimates, reduced predictive validity, and incorrect managerial action. We develop a new multidimensional scaling (MDS) model that estimates spatial market structures from pick-any/J choice data, provides for individual level parameters, and allows for correlations among the choice alternatives across individuals. We provide a Bayesian estimation method that overcomes the traditional problems associated with estimating models with such correlated alternatives. We provide an application to pick-any/J data for various brands of portable telephones. In a comparative analysis, we show that the proposed model outperforms one in which the utilities are assumed to be uncorrelated across choice alternatives.
Year
DOI
Venue
1998
10.1016/S0377-2217(98)00150-7
European Journal of Operational Research
Keywords
Field
DocType
Multidimensional scaling,Context effects,Bayesian analysis,Choice models,Competitive market structure
Econometrics,Probit model,Multidimensional scaling,Consumer choice,Statistics,Bayes estimator,Threshold model,Mathematics,Statistical hypothesis testing,Bayesian probability,Covariance
Journal
Volume
Issue
ISSN
111
2
0377-2217
Citations 
PageRank 
References 
2
0.49
2
Authors
4
Name
Order
Citations
PageRank
Wayne S. DeSarbo1192.85
Youngchan Kim2314.82
Michel Wedel39227.53
Duncan K. H. Fong4368.50