| Social and economic networks play an important role in shaping individuals' behaviors. In this dissertation, we provide a structural econometric analysis of network-related models, including network formation models and social interaction models. In the analysis of network formation models, the goal is to identify and estimate the underlying utility parameters using observed data on network structure, i.e., who is linked with whom. We consider a game-theoretic model of network formation and use pairwise stability, introduced by Jackson and Wolinsky (1996) as the equilibrium condition. The parameters are not point identified when there are only multiple equilibria. We leave the equilibrium selection completely unrestricted and use partial identification. Following Ciliberto and Tamer (2009), we derive bounds on the probability of observing a network. These bounds, however, are computationally infeasible if networks are large. To overcome this computational problem, we propose a novel method based on subnetworks. A subnetwork is the restriction of a network to a subset of the individuals. We derive bounds on the probability of observing a subnetwork, considering only the pairwise stability of the subnetwork rather than the entire network. Under mild assumptions, these subnetwork bounds are computationally feasible as long as we consider only small subnetworks.;As for the social interaction models, we focus on a special case where individuals interact because they can learn from their neighbors about a new technology. We follow the literature on nonparametric identification and provide conditions under which the structural functions and average learning effects in this model can be nonparametrically identified. |
