We provide an asymptotic distribution theory for a class of Generalized Method of Moments estimators that arise in the study of differentiated product markets when the number of observations is associated with the number of products within a given market. We allow for three sources of error: the sampling error in estimating market shares, the simulation error in approximating the shares predicted by the model, and the underlying model error. The limiting distribution of the parameter estimator is normal provided the size of the consumer sample and the number of simulation draws grow at a large enough rate relative to the number of products. We specialise our distribution theory to the Berry, Levinsohn, and Pakes (1995) random coefficient logit model and a pure characteristic model. The required rates differ for these two frequently used demand models. A small Monte Carlo study shows that the difference in asymptotic properties of the two models are reflected in the models’ small sample properties. These differences impact directly on the computational burden of the two models.
In this paper we provide an algorithm for estimating characteristic based demand models from alternative data sources, and apply it to new data on the market for passenger vehicles. We find that, provided care is taken in constructing the demand system and rich enough data are available, the characteristic based model can both rationalize existing results and provide realistic out of sample predictions.
This paper provides a model of firm and industry dynamics that allows for entry, exit and firm-specific uncertainty generating variability in the fortunes of firms. It focuses on the impact of uncertainty arising from investment in research and exploration-type processes. It analyzes the behavior of individual firms exploring profit opportunities in an evolving marketplace and derives optimal policies, including exit, in this environment. Then it adds an entry process and aggregates the optimal behavior of all firms, including potential entrants, into a rational expectations, Markov perfect industry equilibrium, and proves ergodicity of the equilibrium process. Numerical examples are used to illustrate the more detailed characteristics of the stochastic process generating industry structures that result from this equilibrium.