Identification of network effects with spatially endogenous covariates: theory, simulations, and an empirical application

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985998%3A_____%2F25%3A00638658" target="_blank" >RIV/67985998:_____/25:00638658 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11640/25:00638659

Výsledek na webu

<a href="https://doi.org/10.1080/07474938.2025.2514274" target="_blank" >https://doi.org/10.1080/07474938.2025.2514274</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/07474938.2025.2514274" target="_blank" >10.1080/07474938.2025.2514274</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Identification of network effects with spatially endogenous covariates: theory, simulations, and an empirical application

Popis výsledku v původním jazyce

Conventional methods for the estimation of peer, social, or network effects are invalid if individual unobservables and covariates correlate across observations. In this article, we characterize the identification conditions for consistently estimating all the parameters of a spatially autoregressive or linear-in-means model when the structure of social or peer effects is exogenous, but the observed and unobserved characteristics of agents are cross-correlated over some given metric space. We show that identification is possible if the network of social interactions is non overlapping up to enough degrees of separation and the spatial matrix that characterizes the co-dependence of individual unobservables and covariates is known up to a multiplicative constant. We propose a GMM approach for the estimation of the model’s parameters, and we evaluate its performance through Monte Carlo simulations. Finally, we revisit an empirical application about classmates in college. Contrasting with conventional methods, our methodology can estimate zero, non significant peer effects on both academic performance and major choice.

Název v anglickém jazyce

Identification of network effects with spatially endogenous covariates: theory, simulations, and an empirical application

Popis výsledku anglicky

Conventional methods for the estimation of peer, social, or network effects are invalid if individual unobservables and covariates correlate across observations. In this article, we characterize the identification conditions for consistently estimating all the parameters of a spatially autoregressive or linear-in-means model when the structure of social or peer effects is exogenous, but the observed and unobserved characteristics of agents are cross-correlated over some given metric space. We show that identification is possible if the network of social interactions is non overlapping up to enough degrees of separation and the spatial matrix that characterizes the co-dependence of individual unobservables and covariates is known up to a multiplicative constant. We propose a GMM approach for the estimation of the model’s parameters, and we evaluate its performance through Monte Carlo simulations. Finally, we revisit an empirical application about classmates in college. Contrasting with conventional methods, our methodology can estimate zero, non significant peer effects on both academic performance and major choice.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Econometric Reviews

ISSN

0747-4938

e-ISSN

1532-4168

Svazek periodika

44

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

40

Strana od-do

1321-1360

Kód UT WoS článku

001550051300001

EID výsledku v databázi Scopus

2-s2.0-105013293179