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netivreg: Estimation of peer effects in endogenous social networks

Pablo Estrada et al.

Stata Journal2025https://doi.org/10.1177/1536867x251341145article
AJG 2
Weight
0.37

What the paper says

The command netivreg implements the generalized three-stage least-squares estimator developed in Estrada (2022, Causal inference in multilayered networks, PhD thesis) and the generalized method of moments estimator in Chan et al. (2024, Journal of Econometric Methods 13: 205-224) for the endogenous linear-in-means model. The two procedures use full observability of a two-layered multiplex network data structure using Stata’s new multiframes capabilities and Python integration (version 16 and above). Applications of the command include simulated data and three years’ worth of data on peer-reviewed articles published in top general-interest journals in economics.

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https://doi.org/https://doi.org/10.1177/1536867x251341145

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@article{pablo2025,
  title        = {{netivreg: Estimation of peer effects in endogenous social networks}},
  author       = {Pablo Estrada et al.},
  journal      = {Stata Journal},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/1536867x251341145},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.