A Bayesian Multidimensional Scaling Procedure for the Spatial Analysis of Revealed Choice Data

Journal of Econometrics, Volume 89, Issues 1-2, Pages 79-108, 1999

Posted: 9 Jun 2016

Date Written: November 26, 1999

Abstract

We present a new Bayesian formulation of a vector multidimensional scaling procedure for the spatial analysis of binary choice data. The Gibbs sampler is gainfully employed to estimate the posterior distribution of the specified scalar products, bilinear model parameters. The computational procedure allows for the explicit estimation of a covariance matrix which can accommodate violations of IIA due to context effects. In addition, posterior standard errors can be estimated which reflect differential degrees of consumer choice uncertainty and/or brand position instability. A marketing application concerning the analysis of consumers' consideration sets for luxury automobiles is provided to illustrate the use of the proposed methodology.

Keywords: Bayesian analysis, Multidimensional scaling, Choice models, Market structure analysis, Context effects, Consideration sets

Suggested Citation

DeSarbo, Wayne S., A Bayesian Multidimensional Scaling Procedure for the Spatial Analysis of Revealed Choice Data (November 26, 1999). Journal of Econometrics, Volume 89, Issues 1-2, Pages 79-108, 1999, Available at SSRN: https://ssrn.com/abstract=2791773

Wayne S. DeSarbo (Contact Author)

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

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