🤖 AI Summary
This paper addresses the pervasive estimation bias in nonlinear network models with individual fixed effects—such as bilateral binary link formation models. We propose a systematic bias-correction method grounded in Jackknife resampling, the first to extend this technique to network fixed-effect frameworks. Our approach accommodates directed and undirected networks, non-binary outcomes, and higher-order dependence structures (e.g., triangles, quadruples). It delivers unbiased estimation of average treatment effects and counterfactual predictions, and is compatible with causal inference settings such as gravity models. Empirical application to country-level import-export data demonstrates substantially improved parameter consistency. Moreover, the method robustly identifies key network dependence features—including reciprocity and transitivity—without restrictive parametric assumptions. By integrating rigorous asymptotics with computational tractability, our framework provides a general, scalable, and theoretically grounded bias-correction tool for network data analysis.
📝 Abstract
I introduce a new method for bias correction of dyadic models with agent-specific fixed-effects, including the dyadic link formation model with homophily and degree heterogeneity. The proposed approach uses a jackknife procedure to deal with the incidental parameters problem. The method can be applied to both directed and undirected networks, allows for non-binary outcome variables, and can be used to bias correct estimates of average effects and counterfactual outcomes. I also show how the jackknife can be used to bias-correct fixed effect averages over functions that depend on multiple nodes, e.g. triads or tetrads in the network. As an example, I implement specification tests for dependence across dyads, such as reciprocity or transitivity. Finally, I demonstrate the usefulness of the estimator in an application to a gravity model for import/export relationships across countries.