In the pharmaceutical industry, measuring the importance of informative and persuasive roles of detailing is crucial for both drug manufacturers and policy makers. However, little progress has been made in disentangling these two roles of detailing in empirical research. In this paper, we...

We develop a Bayesian Markov chain Monte Carlo (MCMC) algorithm for estimating finite-horizon discrete choice dynamic programming (DDP) models. The proposed algorithm has the potential to reduce the computational burden significantly when some of the state variables are continuous. In a...

To measure the extent of incomplete information about brand qualities faced by consumers, recent research in marketing and economics has extended traditional static choice models to explicitly allow for consumer learning. These models tend to be complicated and make stringent assumptions such as...

We propose a new methodology for structural estimation of infinite horizon dynamic discrete choice models. We combine the Dynamic Programming (DP) solution algorithm with the Bayesian Markov Chain Monte Carlo algorithm into a single algorithm that solves the DP problem and estimates the...

In "Marketing Information: A Competitive Analysis,'' Sarvary and Parker (1997) (S&P) [Marketing Science, 16(1): 24-38] argue that in part of the parameter space that they considered, a reduction in the price of one information product can lead to an increase in demand for another information...

Structural models have become a frontier tool in business and economics research. In this survey, we discuss the literature on structural models for the prescription drug market, which has attracted a significant attention from researchers in marketing and economics, and related fields. The...