arXiv 12 Jul 2026 · Econometrics
arXiv:2607.10613 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a network-adjusted generalized method of moments (NA-GMM) estimator for social interaction models when the observed network may differ from the true interaction network. NA-GMM is a novel penalized GMM approach that allows the elements of the observed interaction matrix to be modified to improve the fit of the moment conditions. To avoid unrestricted network adjustments, the NA-GMM criterion introduces a penalty on the amount of adjustment. Since NA-GMM does not aim to estimate the true interaction network itself, the estimator generally converges to a pseudo-true parameter. For a linear spatial autoregressive model, we prove that the NA-GMM estimator is consistent for the pseudo-true parameter and is asymptotically normally distributed under general moment misspecification. We also prove that a fixed-weight version of the NA-GMM estimator has a desirable bias reduction property relative to conventional GMM without network adjustment. An empirical application to U.S. county-level COVID-19 infection data demonstrates the usefulness of the proposed method.
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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 | Hansen, B.E. and Lee, S (2021) Inference for iterated GMM under misspecification | 0.928 | 4 | 3 | 100% |
| 2 | Hall, A.R. and Inoue, A (2003) The large sample behaviour of the generalized method of moments estimator in misspecified models | 0.811 | 4 | 2 | 100% |
| 3 | Schennach, S. and Starck, V (2026) Optimally-transported generalized method of moments | 0.737 | 3 | 2 | 100% |
| 4 | Kleibergen, F. and Zhan, Z (2025) a | 0.644 | 2 | 2 | 100% |
| 5 | Lewbel, A., Qu, X., and Tang, X (2024) Ignoring measurement errors in social networks | 0.644 | 2 | 2 | 100% |
| 6 | Kelejian, H.H. and Prucha, I.R (2001) On the asymptotic distribution of the moran I test statistic with applications | 0.511 | 4 | 2 | 25% |
| 7 | Andrews, I., Barnhard, H., and Carlson, J (2026) True and pseudo-true parameters | 0.405 | 1 | 1 | 100% |
| 8 | Boucher, V. and Houndetoungan, A (2026) Estimating peer effects using partial network data | 0.405 | 1 | 1 | 100% |
| 9 | Chandrasekhar, A. and Lewis, R (2011) Econometrics of sampled networks | 0.405 | 1 | 1 | 100% |
| 10 | Conley, T.G., Goncalves, S., Kim, M.S., and Perron, B (2023) Bootstrap inference under cross-sectional dependence | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 17 scored citations.