Establishes set identification for dynamic dyadic network formation with fixed effects using dyad-level inequalities and signed subgraph comparisons, with point identification under serially independent known errors and additive individual fixed effects.
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A bagging-based estimator for dyadic networks with fixed effects attains asymptotic normality and the Cramér-Rao bound for both TU and NTU links by using joint MOM, Le Cam refinement, and split-network jackknife.
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Identification in Dynamic Dyadic Network Formation Models with Fixed Effects
Establishes set identification for dynamic dyadic network formation with fixed effects using dyad-level inequalities and signed subgraph comparisons, with point identification under serially independent known errors and additive individual fixed effects.
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Bagging the Network
A bagging-based estimator for dyadic networks with fixed effects attains asymptotic normality and the Cramér-Rao bound for both TU and NTU links by using joint MOM, Le Cam refinement, and split-network jackknife.