A Bayesian approach to the spatial representation of market structure from consumer choice data

Wayne Desarbo, Youngchan Kim, Michel Wedel, Duncan Fong

Research output: Contribution to journalArticle

7 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)285-305
Number of pages21
JournalEuropean Journal of Operational Research
Volume111
Issue number2
DOIs
StatePublished - Dec 1 1998

Fingerprint

Bayesian Approach
Alternatives
Maximum likelihood
Maximum Likelihood
Estimate
Telephone sets
Bayesian Estimation
Standard error
Comparative Analysis
Inconsistent
Model
Test Statistic
Marketing
Degree of freedom
Statistics
Scaling
Sufficient
Necessary
Market
Bayesian approach

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

Cite this

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A Bayesian approach to the spatial representation of market structure from consumer choice data. / Desarbo, Wayne; Kim, Youngchan; Wedel, Michel; Fong, Duncan.

In: European Journal of Operational Research, Vol. 111, No. 2, 01.12.1998, p. 285-305.

Research output: Contribution to journalArticle

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