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Given the advent of basket-level purchasing data of households, choice modelers are actively engaged in the development of statistical and econometric models of multi-category choice behavior of households. This paper reviews current developments in this area of research, discussing the modeling methodologies that have been used, the empirical findings that have emerged so far, and directions for future research. We also motivate the use of Bayesian methods to overcome the computational challenges involved in estimation. Copyright Springer Science + Business Media, Inc. 2005multi-category, multivariate choices, basket data, bayesian estimation,

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In usual practice, researchers specify and estimate brand-choice models from purchase data, ignoring observations in which category incidence does not occur (i.e., no-purchase observations). This practice can be problematic if there are unobservable factors that affect the nopurchase and the brand-choice decisions. When such a correlation exists, it is important to model simultaneously the no-purchase and the brand-choice decisions. The authors propose a model suitable for scanner-panel data in which the no-purchase decision depends on the price, feature, and display of each brand in the category and on household stock of inventory. They link the no-purchase model to the brandchoice outcome through marketing-mix covariates and through unobservables that affect both outcomes. The authors assume that model parameters are heterogeneous across households and allow for a flexible correlation structure between the coefficients in the no-purchase model and those in the brand-choice model. The model formulation is more general than what is possible from either a nested logit model or a translog utility model and from models in which the no-purchase outcome is an additional outcome with the deterministic component of its utility set equal to zero. The authors estimate the proposed model using Bayesian Markov chain Monte Carlo estimation methods. They then apply the estimation methods to scanner-panel data on the cola product category and compare the results with those from the widely used nested logit model. L L J J J J ( )~( ) , † †

Movie sequels, a type of brand extension, are prevalent in today's motion picture industry. Prior literature on brand extensions supports the intuition that attaching established brand names (e.g., titles of box-office hits) to new products decreases advertising costs. We counter this intuition and examine factors that may increase advertising costs for movie sequels. Specifically, we investigate the consequences of asset specificity and bargaining power-concepts from transaction cost economics-in the context of the motion picture industry. We find a positive relationship between the bargaining power of the movie studio's suppliers and advertising expenditures for the movie sequel. We also find evidence of a moderating effect: higher bargaining power of the movie studio dampens the impact of supplier bargaining power on advertising. This is the first study to measure the effect of supplier bargaining power on movie studios' marketing decisions. In a broader context, our findings not only challenge the notion that brand extensions are cost saving, but are also novel in linking transaction cost constructs to the advertising behavior of firms.