Estimating Nonlinear Network Data Models with Fixed Effects

David Hughes

Papers from arXiv.org

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.

Date: 2022-03, Revised 2023-03
New Economics Papers: this item is included in nep-ecm and nep-net
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Related works:
Working Paper: Estimating Nonlinear Network Data Models with Fixed Effects (2021)
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