🤖 AI Summary
Unobserved heterogeneity in network models—such as latent homophily—often leads to biased estimation and misleading policy implications. This study proposes a nonparametric approach that identifies individuals sharing the same unobserved fixed effects through their interactive outcomes, leveraging variation in their observed characteristics to consistently estimate covariate effects without imposing parametric assumptions on the functional form of the fixed effects. Relying solely on interaction data, the method integrates nonparametric identification, fixed-effect control, and large-sample asymptotic theory to yield an estimator with strong theoretical properties. Numerical simulations confirm the estimator’s effectiveness and robustness in handling complex unobserved heterogeneity.
📝 Abstract
Homophily based on observables is widespread in networks. Therefore, homophily based on unobservables (fixed effects) is also likely to be an important determinant of the interaction outcomes. Failing to properly account for latent homophily (and other complex forms of unobserved heterogeneity) can result in inconsistent estimators and misleading policy implications. To address this concern, we consider a network model with nonparametric unobserved heterogeneity, leaving the role of the fixed effects unspecified. We argue that the interaction outcomes can be used to identify agents with the same values of the fixed effects. The variation in the observed characteristics of such agents allows us to identify the effects of the covariates, while controlling for the fixed effects. Building on these ideas, we construct several estimators of the parameters of interest and characterize their large sample properties. Numerical experiments illustrate the usefulness of the suggested approaches and support the asymptotic theory.