Yingxing Li, Aureo De Paula, Weining Wang
arXiv 21 Jul 2026 · Econometrics
arXiv:2607.19625 · PDF · Extracted main text
This paper analyzes spillover effects in spatial (network) models when the neighborhood (adjacency) matrix is contaminated by measurement error from reporting, aggregation, or disclosure imperfections, leading to inconsistent estimation of network effects. We introduce a regularization framework for the latent network that allows for sparse and/or low-rank structure and accommodates potential correlation between measurement errors and outcomes. We propose two estimators: (i) a two-stage procedure that first denoises the adjacency matrix and then incorporates the purified network into a regression analysis, and (ii) a Generalized Method of Moments (GMM) estimator that jointly estimates regression parameters and refines the network structure. We then establish strictly improved consistency rates for the spillover effect estimator relative to naive estimation ignoring measurement error. Simulations demonstrate that, in the presence of noisy networks, our approach reduces the root mean squared error of spillover estimates relative to conventional methods by approximately $50-80%$. We apply our framework to examine the international spillover of economic growth, and the tax competition across U.S. states, illustrating that denoising might restore Leontief stability and yields improved estimates of spillovers.
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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 | Lewbel, Arthur and Qu, Xi and Tang, Xun (2024) Ignoring measurement errors in social networks | 0.969 | 11 | 5 | 91% |
| 2 | Acemoglu, Daron and Carvalho, Vasco M. and Ozdaglar, Asuman and Tahb… (2012) The network origins of aggregate fluctuations | 0.843 | 5 | 3 | 60% |
| 3 | Agarwal, Alekh and Negahban, Sahand and Wainwright, Martin J (2012) Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions | 0.737 | 5 | 3 | 40% |
| 4 | Anand, Kartik and Craig, Ben and von Peter, Goetz (2015) Filling in the Blanks: Network Structure and Interbank Contagion | 0.737 | 3 | 2 | 100% |
| 5 | Aureo de Paula and Imran Rasul and Pedro Souza (2025) Identifying network ties from panel data: Theory and an application to tax competition self | 0.737 | 3 | 2 | 100% |
| 6 | Wainwright, Martin J (2019) High-dimensional statistics: A non-asymptotic viewpoint | 0.667 | 9 | 2 | 44% |
| 7 | Bramoullé, Yann and Djebbari, Habiba and Fortin, Bernard (2009) Identification of peer effects through social networks | 0.644 | 2 | 2 | 100% |
| 8 | Luis E. Candelaria and Takuya Ura (2023) Identification and inference of network formation games with misclassified links | 0.644 | 2 | 2 | 100% |
| 9 | Candès, Emmanuel J. and Li, Xiaodong and Ma, Yi and Wright, John (2011) Robust principal component analysis? | 0.644 | 2 | 2 | 100% |
| 10 | Fisman, Raymond and Wei, Shang-Jin (2004) Tax Rates and Tax Evasion: Evidence from “Missing Imports” in China | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 77 scored citations.