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Identification and Estimation of Network Models with Nonparametric Unobserved Heterogeneity

Andrei Zeleneev

arXiv 6 Feb 2026 · Econometrics

arXiv:2602.06885 · PDF · DOI · OpenAlex · Extracted main text

Abstract

Homophily based on observables is widespread in networks. Therefore, homophily based on unobservables (fixed effects) is also likely to be an important determinant of the interaction outcomes. Failing to properly account for latent homophily (and other complex forms of unobserved heterogeneity) can result in inconsistent estimators and misleading policy implications. To address this concern, we consider a network model with nonparametric unobserved heterogeneity, leaving the role of the fixed effects unspecified. We argue that the interaction outcomes can be used to identify agents with the same values of the fixed effects. The variation in the observed characteristics of such agents allows us to identify the effects of the covariates, while controlling for the fixed effects. Building on these ideas, we construct several estimators of the parameters of interest and characterize their large sample properties. Numerical experiments illustrate the usefulness of the suggested approaches and support the asymptotic theory.

Citation extraction

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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
Zhang2017unmatched citation key Zhang20171.000144100%
beyhum2024inferenceunmatched citation key beyhum2024inference1.000103100%
Graham2017unmatched citation key Graham20171.00073100%
Chatterjee2015unmatched citation key Chatterjee20151.00065100%
Klopp2017unmatched citation key Klopp20171.00054100%
Gao2015unmatched citation key Gao20151.00053100%
li2019nearestunmatched citation key li2019nearest0.92844100%
freeman2023linearunmatched citation key freeman2023linear0.92843100%
gao2020nonparametricunmatched citation key gao2020nonparametric0.92843100%
bonhomme2022discretizingunmatched citation key bonhomme2022discretizing0.87482100%

Showing the top 10 of 66 scored citations. 10 of these could not be matched to a bibliography entry, so only the citation key is shown.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1The Network Propensity Score: Spillovers, Homophily, and Selection into Treatment0.84333
2A Semiparametric Network Formation Model with Unobserved Linear Heterogeneity0.81142
3Improving control over unobservables with network data0.73732
4A Simple and Computationally Trivial Estimator for Grouped Fixed Effects Models0.64441
5Inferring Treatment Effects in Large Panels by Uncovering Latent Similarities0.52992
6Tractable Estimation of Nonlinear Panels with Interactive Fixed Effects0.51121
7Low-Rank Approximations of Nonseparable Panel Models0.40511
8Linear Panel Regressions with Two-Way Unobserved Heterogeneity0.40511
9Robust Estimation and Inference in Panels with Interactive Fixed Effects0.40511
10Identifying Socially Disruptive Policies0.40511