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Abstract:
I study a social interaction model in which agent behavior is influenced by latent drivers of link formation in a network. Rather than specify or fit a parametric model of network formation, I introduce a new methodology based on matching pairs of agents with similar columns of the squared adjacency matrix, the ijth entry of which contains the number of other agents linked to both agents i and j. The intuition is that for a large class of network formation models the columns of this matrix characterize all of the identifiable information about individual linking behavior. In the paper, I first describe the model and formalize this intuition. I then introduce the estimators and characterize their large sample properties.
