This research program revolves around three different but related projects in discrete choice modeling and estimation. The common theme is the modeling of consumer choice behavior, and the estimation of models of consumer behavior on revealed preference data sets. With the availability of electronic scanner panel data, choice models have become very important for understanding consumer behavior. For example, such models may be used to evaluate the impact of the marketing mix on consumer purchasing behavior, and to assess the patterns of competition in the market place. The three research projects in this study involve the use of recently developed simulation estimation techniques. In the model of the first project, consumer choice probabilities are high dimensional integrals over unobserved prices and coupon values, as well as over stochastic preference shocks. Thus, Monte-Carlo methods must be used to simulate the likelihood function in the model. In the second and third projects, both solution of the dynamic programming problem and construction of the likelihood function require high dimensional integrations over stochastic process for prices, preference shocks, etc. In these projects we will use MonteCarlo methods both to solve consumers' dynamic programming problems and to simulate the likelihood function.
