A Structural Model for the Coevolution of Networks and Behavior
This paper introduces a structural model for the coevolution of networks and behavior. The microfoundation of our model is a network game where agents adjust actions and network links in a stochastic best-response dynamics with a utility function allowing for both strategic externalities and unobserved heterogeneity. We show the network game admits a potential function and the coevolution process converges to a unique stationary distribution characterized by a Gibbs measure. To bypass the evaluation of the intractable normalizing constant in the Gibbs measure, we adopt the Double Metropolis-Hastings algorithm to sample from the posterior distribution of the structural parameters. To illustrate the empirical relevance of our structural model, we apply it to study R&D investment and collaboration decisions in the chemicals and pharmaceutical industry and find a positive knowledge spillover effect. Finally, our structural model provides a tractable framework for a long-run key player analysis.