This paper outlines methodological frameworks for conducting research and development with agribusiness supply chains in transitional economies where the objective is to improve the competitiveness of the supply chains in a global environment. The key difficulty when operating with supply chains...

The workhorse brand choice models in marketing are the multinomial logit (MNL) and nested multinomial logit (NMNL). These models place strong restrictions on how brand share and purchase incidence price elasticities are related. In this paper, we propose a new model of brand choice, the “price...

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...

Until recently, computational constraints forced researchers in the discrete choice area to limit themselves to very simple statistical models, such as the multinomial logit (MNL), in which choice probabilities could be evaluated quickly on a computer. But the MNL only makes sense as a...

We develop a model of household demand for frequently purchased consumer goods that are branded, storable and subject to stochastic price fluctuations. Our framework accounts for how inventories and expectations of future prices affect current period purchase decisions. We estimate our model...

The aim of this paper is to explore the links between brand equity, consumer learning and consumer choice processes in general, and considering two recent trends in the market place: store brands and the Internet. We first review the advances that have occurred in brand equity research in...

In recent years, major advances have taken place in three areas of random utility modeling: (1) semiparametric estimation, (2) computational methods for multinomial probit models, and (3) computational methods for Bayesian stimation. This paper summarizes these developments and discusses their...

Simulation estimation in the context of panel data, limited dependent-variable (LDV) models poses formidable problems that are not present in the crosssection case. Nevertheless, a number of practical simulation estimation methods have been proposed and implemented for panel data LDV models....

Our goal in this chapter is to explain concretely how to implement simulation methods in a very general class of models that are extremely useful in applied work: dynamic discrete choice models where one has available a panel of multinomial choice histories and partially observed payoffs....

This study develops practical methods for Bayesian nonparametric inference in regression models. The emphasis is on extending a nonparametric treatment of the regression function to the full conditional distribution. It applies these methods to the relationship of earnings of men in the United...