netivreg: Estimation of peer effects in endogenous social networks

Pablo Estrada (), Juan Estrada (), Kim P. Huynh (), David Jacho-Chávez () and Leonardo Sánchez-Aragón ()
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Pablo Estrada: Emory University
Juan Estrada: Analysis Group Economic Consulting
Kim P. Huynh: Bank of Canada
David Jacho-Chávez: Emory University
Leonardo Sánchez-Aragón: ESPOL University

Stata Journal, 2025, vol. 25, issue 2, 344-373

Abstract: 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.

Keywords: netivreg; instrumental variables; multiplex networks; network effects; Python; endogenous network; peer effects; generalized three-stage least squares; gmm (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1177/1536867X251341145

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