Identification in Dynamic Dyadic Network Formation Models with Fixed Effects

TL;DR

This paper develops identification results for a dynamic dyadic network formation model with fixed effects, observed covariates, and local network statistics, combining multiple approaches to handle unobserved heterogeneity.

Contribution

It introduces a unified framework that accommodates various network effects and fixed effects, sharpening identification through novel inequalities and algebraic differences.

Findings

Identification results for dynamic dyadic network models with fixed effects.

Conditions for point identification under i.i.d. logit shocks.

Framework incorporates homophily, transitivity, and local subgraph effects.

Abstract

This paper establishes (set) identification results in a dynamic dyadic network formation model with time-varying observed covariates, lagged local network statistics, and unobserved heterogeneity in the form of fixed effects. Our framework accommodates observed-covariate homophily, transitivity through common friends, second-order or indirect-friend effects, and more general local subgraph statistics within a single dynamic index model. The analysis combines two complementary ways of handling fixed effects: inequalities that integrate out time-invariant dyad heterogeneity by treating each dyad as a short panel, and signed-subgraph comparisons that difference out fixed effects algebraically through intertemporal variation within each dyad. We show that the semiparametric identifying restrictions can be sharpened using either or both of the following assumptions: (i) error distribution is serially independent with a known distribution, (ii) pairwise fixed effect takes the form of additive individual fixed effects. Combining (i) and (ii) under i.i.d. logit shocks, we obtain an exact conditional logit representation and provide sufficient conditions for point identification.