arXiv 3 Apr 2026 · Econometrics
arXiv:2604.03171 · PDF · DOI · OpenAlex · Extracted main text
Sampled network data are widely used in empirical research because collecting complete network information is costly. However, empirical analyses based on sampled networks may lead to biased estimators. We propose a nonparametric imputation method for sampled networks and show that empirical analyses based on imputed networks yield consistent estimates. Our approach imputes missing network links by combining a projection onto covariates with a local two-way fixed-effects regression. The method avoids parametric assumptions, does not rely on low-rank restrictions, and flexibly accommodates both observed covariates and unobserved heterogeneity. We establish entrywise convergence rates for the imputed matrix and prove the consistency of generalized method of moments (GMM) estimators based on imputed networks. We further derive the convergence rate of the corresponding estimator in the linear-in-means peer-effects model. Simulations show strong performance of our method both in terms of imputation accuracy and in downstream empirical analysis. We illustrate our method with an application to the microfinance network data of Banerjee et al. (2013).
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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 | Feng, Yingjie (2023) Optimal estimation of large-dimensional nonlinear factor models | 1.000 | 13 | 3 | 100% |
| 2 | Banerjee, Abhijit and Chandrasekhar, Arun G and Duflo, Esther and Ja… (2013) The diffusion of microfinance | 1.000 | 9 | 4 | 100% |
| 3 | Li, Tianxi and Wu, Yun-Jhong and Levina, Elizaveta and Zhu, Ji (2023) Link prediction for egocentrically sampled networks | 0.965 | 10 | 3 | 90% |
| 4 | Graham, Bryan S and Niu, Fengshi and Powell, James L (2021) Minimax risk and uniform convergence rates for nonparametric dyadic regression | 0.950 | 7 | 4 | 86% |
| 5 | Chandrasekhar, Arun and Lewis, Randall (2011) Econometrics of sampled networks | 0.874 | 9 | 2 | 100% |
| 6 | Zhang, Yuan and Levina, Elizaveta and Zhu, Ji (2017) Estimating network edge probabilities by neighbourhood smoothing | 0.874 | 6 | 3 | 67% |
| 7 | Deaner, Ben and Hsiang, Chen-Wei and Zeleneev, Andrei (2025) Inferring Treatment Effects in Large Panels by Uncovering Latent Similarities | 0.874 | 6 | 2 | 100% |
| 8 | Bai, Jushan and Ng, Serena (2021) Matrix completion, counterfactuals, and factor analysis of missing data | 0.840 | 9 | 2 | 89% |
| 9 | Graham, Bryan S (2017) An econometric model of network formation with degree heterogeneity | 0.811 | 4 | 2 | 100% |
| 10 | Beyhum, Jad and Mugnier, Martin (2024) Inference after discretizing time-varying unobserved heterogeneity | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 54 scored citations.