Ronald L. Goettler

Does AMD Spur Intel to Innovate More

We estimate an equilibrium model of dynamic oligopoly with durable goods and endogenous innovation to examine the effect of competition on innovation in the personal computer microprocessor industry. Firms make dynamic pricing and investment decisions while consumers make dynamic upgrade decisions, anticipating product improvements and price declines. Consistent with Schumpeter, we find that the rate of innovation in product quality would be 4.2 percent higher without AMD present, though higher prices would reduce consumer surplus by

Structural Applications of the Discrete Choice Model

12 billion per year. Comparative statics illustrate the role of product durability and provide implications of the model for other industries.

New Drug Diffusion When Forward-Looking Physicians Learn from Patient Feedback and Detailing

A growing body of empirical literature uses structurally-derived economic models to study the nature of competition and to measure explicitly the economic impact of strategic policies. While several approaches have been proposed, the discrete choice demand system has experienced wide usage. The heterogeneous, or “mixed”, logit in particular has been widely applied due to its parsimonious structure and its ability to capture flexibly substitution patterns for a large number of differentiated products.We outline the derivation of the heterogeneous logit demand system. We then present a number of applications of such models to various data sources. Finally, we conclude with a discussion of directions for future research in this area.

Informed traders and limit order markets.

The authors study physicians’ prescription choices when uncertainty about drug efficacy is resolved through two channels: firms’ marketing activities (e.g., detailing) and patients’ experiences with the drugs. They first provide empirical evidence that suggests that the well-understood information incentive for physicians to experiment with new drugs is reduced when physicians anticipate future detailing. Therefore, increased detailing activity triggers opposing forces: Adoption is hastened as physicians become informed (assuming prior knowledge is initially low) and slows as they reduce experimentation and instead obtain information from detailing at no cost. The authors then estimate a dynamic Bayesian learning model that embodies these trade-offs using physician-level data on prescription choices and detailing received in the months surrounding the introduction of two erectile dysfunction drugs, Levitra and Cialis. Detailing elasticities are lower when physicians anticipate changes in detailing activity than when such changes are unexpected. Accordingly, the authors conclude that to maximize the effect of detailing, firms should avoid announcing increases in detailing activities.