A Factor-Analytic Probit Model for Representing the Market Structure in Panel Data

Abstract

Internal market structure analysis infers both brand attributes and consumer preferences for those attributes from preference or choice data. The authors exploit a new method for estimating probit models from panel data to infer market structures that can be displayed in few dimensions, even though the model can represent every possible vector of purchase probabilities. The result outperforms each of several other models, including Choice Map, SCULPTRE, and Chintagunta's latent class model in terms of goodness of fit, predictive validity, and face validity for a detergent data set. Because theirs is the only market structure model to outperform the structureless Dirichlet-multinomial stochastic brand choice model, the other methods cannot claim to have recovered market structure for these data.