Advances in computation,statistical methods,and testing of discrete choice models

This paper gives a brief overview of recent developments in computation, estimation, and statistical testing of choice models, with marketing applications. Topics include statistical models for discrete panel data with heterogeneous decision-makers, simulation methods for estimation of high-dimension multinomial probit models, specification tests for model structure and for brand and purchase clustering, and innovations in numerical analysis for estimation and forecasting. In collaboration with Denis Bolduc, David Bunch, Michael Keane, Don Kridel, and Steve Stern.     

Consideration set influences on consumer decision-making and choice: Issues,models, and suggestions

This paper affords a stylized view of individual consumer choice decision-making appropriate to the study of many marketing decisions. It summarizes issues relating to consideration set effects on consumer judgment and choice. It discusses whether consideration sets really exist and, if so, the factors that affect their composition, structure, and role in decision-making. It examines some new developments in the measurement and modeling of consideration set effects on decision-making. The paper concludes with suggestions for needed research. The authors wish to acknowledge the numerous ideas and perspectives contributed by the other members of the Banff Symposium workshop:Mukesh Bhargava (University of Alberta),Bill Black (Louisiana State University),Gary Gaeth (University of Iowa),Hotaka Katahira (University of Tokyo, Japan),Gilles Laurent (Centre HEC-ISA, France),Irwin Levin (University of Iowa),David Midgley (Australian Graduate School of Management),Thomas Novak (Southern Methodist University), andJames Wiley (University of Alberta). This paper has benefited greatly from their contributions.     

A simple method for estimating preference parameters for individuals

This paper demonstrates a method for estimating logit choice models for small sample data, including single individuals, that is computationally simpler and relies on weaker prior distributional assumptions compared to hierarchical Bayes estimation. Using Monte Carlo simulations and online discrete choice experiments, we show how this method is particularly well suited to estimating values of choice model parameters from small sample choice data, thus opening this area to the application of choice modeling. For larger sample sizes of approximately 100–200 respondents, preference distribution recovery is similar to hierarchical Bayes estimation of mixed logit models for the examples we demonstrate. We discuss three approaches for specifying the conjugate priors required for the method: specifying priors based on existing or projected market shares of products, specifying a flat prior on the choice alternatives in a discrete choice experiment, or adopting an empirical Bayes approach where the prior choice probabilities are taken to be the average choice probabilities observed in a discrete choice experiment. We show that for small sample data, the relative weighting of the prior during estimation is an important consideration, and we present an automated method for selecting the weight based on a predictive scoring rule.