their useful comments. I would also like to thank Taylan Yildiz for research assistance on an early variant of this paper titled “A Structural Analysis of Joint Decision-Making. ” The usual disclaimer applies. Demand Estimation with Social Interactions an

This paper develops a model for the estimation and analysis of demand in the context of social interactions. Decisions made by a group of customers are modeled to be an equilibrium outcome of an empirical discrete game, such that all group members must be satisfied with chosen outcomes. The game-theoretic approach assists estimation by allowing us to account for the endogeneity of group members ’ decisions, while also serving as a managerial tool that can simulate equilibrium outcomes for the group when the firm alters the marketing mix to the group. The model builds upon the existing literature on empirical models of discrete games by introducing a random coefficients heterogeneity distribution. Monte Carlo simulations reveal that including the heterogeneity resolves the endogenous group formation bias commonly noted in the social interactions literature. By estimating the heterogeneous equilibrium model using Bayesian hierarchical MCMC, we can also recover some parameters at the individual-level to evaluate group specific characteristics and targeted marketing strategies. To validate the model and illustrate its implications, we apply it to a data set of groups o