Identification and Estimation of Network Models with Nonparametric Unobserved Heterogeneity

About this event

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, in general) can result in inconsistent estimators and misleading policy implications. To address this concern, I consider a network model with nonparametric unobserved heterogeneity, leaving the role of the fixed effects and the nature of their interaction unspecified. I argue that the outcomes of the interactions can be used to identify agents with the same values of the fixed effects. The variation in the observed characteristics of such agents allows me to identify the effects of the covariates, while controlling for the impact of the fixed effects. Building on these ideas, I construct several estimators of the parameters of interest and characterize their large sample properties. The suggested approach is not specific to the network context and applies to general two-way models with nonparametric unobserved heterogeneity, including large panels. A Monte-Carlo experiment illustrates the usefulness of the suggested approaches and supports the large sample theory findings.