arXiv 29 Mar 2022 · Econometrics · publishedJournal of Econometrics (2025) · 2 citations (OpenAlex)
arXiv:2203.15603 · PDF · DOI · OpenAlex · Extracted main text
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.
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Penalized Likelihood for Dyadic Network Formation Models with Degree Heterogeneity | 0.811 | 4 | 2 |
| 2 | Functional Differencing in Networks | 0.405 | 1 | 1 |
| 3 | Dyadic data with ordered outcome variables | 0.405 | 1 | 1 |
| 4 | 0.5cmLow-Rank Estimation of Nonlinear Panel Data Models | 0.405 | 1 | 1 |