We propose a methodology to estimate structural models of product line competition. This methodology enables researchers to estimate demand systems accounting for the endogeneity of product introductions, an issue which is typically ignored in the empirical literature in marketing and economics. In particular, we find that not accounting for this form of endogeneity leads to overoptimistic estimates of total demand due to sample selection bias. More importantly, biased estimates of demand can generate misleading managerial recommendations and inaccurate inferences about consumer welfare. In addition, the formulation of a structural model of product line competition enables a researcher to consider policy simulations aimed at studying the consequences of changes in a number of structural factors, such as consumer preferences, cost structure, ownership and capacity. The model under analysis jointly considers the interplay between consumer preferences, pricing and product line decisions. Consumer demand is characterized by a utility maximization process with unobserved heterogeneity in consumer preferences. Price decisions are assumed to be the outcome of a Bertrand-Nash game among firms offering differentiated products. Product line decisions are modeled using a Bayesian-Nash equilibrium concept where firms form beliefs about the profits of their competitors and anticipate the prices and demand they would observe for any given set of products that could be introduced in the market. The estimation approach takes advantage of recent advances in parallel computing by decomposing some of the most computationally intensive steps into a series of independent and much smaller problems. The methodology is illustrated using both simulated and real data.