Research

Published Papers

Working Papers

  • Robust Inference in Locally Misspecified Bipartite Networks, with Yichong Zhang
    • Submitted
    • Abstract:

    • This paper introduces a methodology to conduct robust inference in bipartite networks under local misspecification. We focus on a class of dyadic network models with misspecified conditional moment restrictions. The framework of misspecification is local, as the effect of misspecification varies with the sample size. We utilize this local asymptotic approach to construct a robust estimator that is minimax optimal for the mean square error within a neighborhood of misspecification. Additionally, we introduce bias-aware confidence intervals that account for the effect of the local misspecification. These confidence intervals have the correct asymptotic coverage for the true parameter of interest under sparse network asymptotics. Monte Carlo experiments demonstrate that the robust estimator performs well in finite samples and sparse networks. As an empirical illustration, we study the formation of a scientific collaboration network among economists.

  • A Semiparametric Network Formation Model with Unobserved Linear Heterogeneity
    • Revise and Resubmit, Econometrics Journal
    • Abstract:

    • This paper analyzes a semiparametric model of network formation in the presence of unobserved agent-specific heterogeneity. Its objective is to identify and estimate the preference parameters associated with observed homophily when the distribution of the unobserved factors is not parametrically specified. This paper offers two main contributions to the literature on network formation. First, it establishes a new point identification result for the vector of parameters that relies on the existence of a special regressor. The identification proof is constructive and characterizes a closed form for the parameter of interest. Second, it introduces a two-step semiparametric estimator with a first-step kernel estimator. This estimator is consistent and has a limiting normal distribution under sparse network asymptotics. Monte Carlo experiments demonstrate that the estimator performs well in finite samples. Finally, the methodology is implemented to estimate the homophily parameters in a friendship network using the Add Health dataset.

Work in Progress

Social Interactions under Cluster Dependance, with Amedeo Andriollo