arXiv 8 Apr 2026 · Econometrics
arXiv:2604.07488 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Gao, W. Y., M. Li, and Z. Xu (2026) Tractable Identification of Strategic Network Formation Models with Unobserved Heterogeneity self | 1.000 | 11 | 3 | 100% |
| 2 | Graham, B. S (2017) An Econometric Model of Network Formation with Degree Heterogeneity | 1.000 | 10 | 4 | 100% |
| 3 | Gao, W. Y. and R. Wang (2026) Identification in nonlinear dynamic panel models under partial stationarity self | 1.000 | 9 | 3 | 100% |
| 4 | Graham, B. S (2016) Homophily and Transitivity in Dynamic Network Formation, Tech | 1.000 | 6 | 3 | 100% |
| 5 | Candelaria, L. E (2017) A Semiparametric Network Formation Model with Multiple Linear Fixed Effects, Working paper, The University of Edinburgh | 0.405 | 1 | 1 | 100% |
| 6 | de Paula, A (2020) a): Econometric Models of Network Formation | 0.405 | 1 | 1 | 100% |
| 7 | de Paula, A (2020) b): Strategic network formation, in | 0.405 | 1 | 1 | 100% |
| 8 | Gao, W. Y (2020) Nonparametric identification in index models of link formation self | 0.405 | 1 | 1 | 100% |
| 9 | Gao, W. Y., M. Li, and S. Xu (2023) Logical differencing in dyadic network formation models with nontransferable utilities self | 0.405 | 1 | 1 | 100% |
| 10 | Graham, B. S (2020) Dyadic regression, in | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 13 scored citations.