Juan Estrada, Kim Huynh, David Jacho-Chavez, Leonardo Sanchez-Aragon
arXiv 2 Aug 2026 · Econometrics
arXiv:2608.01421 · PDF · Extracted main text
A novel method to estimate social effect coefficients in the popular so-called linear-in-means regression model in the Social Sciences is presented here that utilizes non-experimental multidimensional network data. The procedure can accommodate social interactions that correlate with the error in the model by making use of a different set of network links among the same observations that are exogenous in the traditional sense. In particular, the full observability of a two-layered multiplex network data structure is assumed here to propose a new Generalized 3-Stage Least Squares (G3SLS) estimator that is consistent, asymptotically normally distributed, and also easy to implement using widely-used existing statistical software because of its closed-form definition. The underlying assumptions are general enough to accommodate common problems with observational data such as measurement error, simultaneity, and unobserved heterogeneity. Monte Carlo exercises confirm the good small sample performance of the proposed G3SLS estimator in these scenarios. An empirical application finds positive and significant peer effects in citations among research articles published in top general-interest journals in economics.
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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 | Chan, TszKin Julian, Juan Estrada, Kim Huynh, David Jacho-Chavez, Ch… (2024) Estimating Social Effects with Randomized and Observational Network Data self | 1.000 | 8 | 4 | 100% |
| 2 | Estrada, Pablo, Juan Estrada, Kim Huynh, David Jacho-Chavez, and Leo… (2025) netivreg: Estimation of Peer Effects in Endogenous Social Networks self | 0.843 | 3 | 3 | 100% |
| 3 | Boccaletti, S., G. Bianconi, R. Criado, C. I. del Genio, J. Gómez-Ga… (2014) The Structure and Dynamics of Multilayer Networks | 0.811 | 4 | 2 | 100% |
| 4 | Estrada, Juan (2022) Causal Inference in Multilayered Networks self | 0.737 | 3 | 3 | 67% |
| 5 | Johnsson, Ida and Hyungsik Roger Moon (2021) Estimation of Peer Effects in Endogenous Social Networks: Control Function Approach | 0.737 | 3 | 2 | 100% |
| 6 | Carrell, Scott E, Bruce I Sacerdote, and James E West (2013) From Natural Variation to Optimal Policy? The Importance of Endogenous Peer Group Formation | 0.737 | 3 | 2 | 100% |
| 7 | Erdös, P and A Rényi (1959) On Random Graphs | 0.644 | 4 | 1 | 100% |
| 8 | Atkisson, Curtis, Piotr J. Górski, Matthew O. Jackson, Janusz A. Hoy… (2020) Why Understanding Multiplex Social Network Structuring Processes Will Help Us Better Understand the Evolution of Human Behavior | 0.644 | 2 | 2 | 100% |
| 9 | Kelejian, Harry H. and Ingmar R. Prucha (1998) A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Distur… | 0.644 | 2 | 2 | 100% |
| 10 | Kelejian, Harry H. and Ingmar R. Prucha (1999) A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 43 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Estimating Peer Influence in Multilayer Networks | 0.405 | 1 | 1 |