Assessing Heterogeneity in Discrete Choice Models Using a Dirichlet Process Prior
Jin Gyo Kim
Massachusetts Institute of Technology (MIT)
University of Toronto - Rotman School of Management
University of Michigan at Ann Arbor - Marketing
Review of Marketing Science, Vol. 2, Article 1, 2004
The finite normal mixture model has emerged as a dominant methodology for assessing heterogeneity in choice models. Although it extends the classic mixture models by allowing within component variablility, it requires that a relatively large number of models be separately estimated and fairly difficult test procedures to determine the "correct" number of mixing components. We present a very general formulation, based on Dirichlet Process Piror, which yields the number and composition of mixing components a posteriori, obviating the need for post hoc test procedures and is capable of approximating any target heterogeneity distribution. Adapting Stephens' (2000) algorithm allows the determination of "substantively" different clusters, as well as a way to sidestep problems arising from label-switching and overlapping mixtures. These methods are illustrated both on simulated data and A.C. Nielsen scanner panel data for liquid detergents. We find that the large number of mixing components required to adequately represent the heterogeneity distribution can be reduced in practice to a far smaller number of segments of managerial relevance.
Accepted Paper Series
Date posted: February 7, 2004
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