The primary emphasis of quantitative marketing is on the evaluation of firm policies for determination of marketing variables as well as the optimization of these policies. Thus, our methods of inference must apply to decision problems and to policy evaluation. In addition, the data we have...

Observational data on either individual or aggregate demand is often not sufficient to identify consumer preferences due to lack of variation in prices or product features, or the desire to study product features not currently available. Choice-based conjoint analysis offers a solution to this...

We present a Bayesian approach for analyzing aggregate level sales data in a market with differentiated products. We consider the aggregate share model proposed by Berry, Levinsohn and Pakes (1995) which introduces a common demand shock into an aggregated random coefficient logit model. A full...

Instrumental variables (IVs) are widely used to identify effects in models with potentially endogenous explanatory variables. In many cases, the instrument exclusion restriction that underlies the validity of the usual IV inference holds only approximately; that is, the instruments are...

We develop a Bayesian semi-parametric approach to the instrumental variable problem. We assume linear structural and reduced form equations, but model the error distributions non-parametrically. A Dirichlet process prior is used for the joint distribution of structural and instrumental variable...

The conventional wisdom in economic theory holds that switching costs make markets less competitive. This paper challenges this claim. We find that steady-state equilibrium prices may fall as switching costs are introduced into a simple model of dynamic price competition that allows for...

Demand for product characteristics is examined within the context of models that allow for both corner and interior solutions corresponding to zero and non-zero demand. Product attribute information is associated with marginal utility and curvature (satiation) parameters of various utility...

Hierarchical Bayes models free researchers from computational constraints and allow for the development of more realistic models of buyer behavior and decision making. Moreover, this freedom enables exploration of marketing problems that have proven elusive over the years, such as models for...

We review the importance of structural modeling for the purposes of simulation of the effects of changes in marketing policy. We pay particular attention to the aspects of structural modeling which present unique challenges for researchers in marketing. We also catalogue some of the advantages...

The recent marketing literature reflects a growing interest in structural models, stemming from: 1) the desire to test a variety of behavioral theories with market data, and 2) recent developments that facilitate estimation of and inference for these models. Whether one should always go through...