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Identification and Estimation of a Partially Linear Regression Model Using Network Data

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  • Eric Auerbach

Abstract

I study a regression model in which one covariate is an unknown function of a latent driver of link formation in a network. Rather than specify and fit a parametric network formation model, I introduce a new method based on matching pairs of agents with similar columns of the squared adjacency matrix, the ijth entry of which contains the number of other agents linked to both agents i and j. The intuition behind this approach is that for a large class of network formation models the columns of the squared adjacency matrix characterize all of the identifiable information about individual linking behavior. In this paper, I describe the model, formalize this intuition, and provide consistent estimators for the parameters of the regression model.

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  • Eric Auerbach, 2022. "Identification and Estimation of a Partially Linear Regression Model Using Network Data," Econometrica, Econometric Society, vol. 90(1), pages 347-365, January.
    • Handle: RePEc:wly:emetrp:v:90:y:2022:i:1:p:347-365
    DOI: 10.3982/ECTA19794
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    References listed on IDEAS

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    1. Sacerdote, Bruce, 2011. "Peer Effects in Education: How Might They Work, How Big Are They and How Much Do We Know Thus Far?," Handbook of the Economics of Education, in: Erik Hanushek & Stephen Machin & Ludger Woessmann (ed.), Handbook of the Economics of Education, edition 1, volume 3, chapter 4, pages 249-277, Elsevier.
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    5. Áureo de Paula, 2020. "Econometric Models of Network Formation," Annual Review of Economics, Annual Reviews, vol. 12(1), pages 775-799, August.
    6. Eric Auerbach, 2019. "Identification and Estimation of a Partially Linear Regression Model using Network Data," Papers 1903.09679, arXiv.org, revised Jun 2021.
    7. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 531-542.
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    9. Tiziano Arduini & Eleonora Patacchini & Edoardo Rainone, 2015. "Parametric and Semiparametric IV Estimation of Network Models with Selectivity," EIEF Working Papers Series 1509, Einaudi Institute for Economics and Finance (EIEF), revised Oct 2015.
    10. Eric Auerbach, 2021. "Identification and Estimation of a Partially Linear Regression Model using Network Data: Inference and an Application to Network Peer Effects," Papers 2105.10002, arXiv.org.
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    Cited by:

    1. Hahn, Jinyong & Liao, Zhipeng & Ridder, Geert & Shi, Ruoyao, 2023. "The influence function of semiparametric two-step estimators with estimated control variables," Economics Letters, Elsevier, vol. 231(C).
    2. Shuyang Sheng & Xiaoting Sun, 2023. "Social Interactions with Endogenous Group Formation," Papers 2306.01544, arXiv.org.
    3. Jochmans, Koen, 2023. "Peer effects and endogenous social interactions," Journal of Econometrics, Elsevier, vol. 235(2), pages 1203-1214.
    4. Brice Romuald Gueyap Kounga, 2023. "Identification and Estimation of a Semiparametric Logit Model using Network Data," Papers 2310.07151, arXiv.org.
    5. Gao, Wayne Yuan & Li, Ming & Xu, Sheng, 2023. "Logical differencing in dyadic network formation models with nontransferable utilities," Journal of Econometrics, Elsevier, vol. 235(1), pages 302-324.
    6. Candelaria, Luis E. & Ura, Takuya, 2023. "Identification and inference of network formation games with misclassified links," Journal of Econometrics, Elsevier, vol. 235(2), pages 862-891.
    7. Alejandro Sanchez-Becerra, 2022. "The Network Propensity Score: Spillovers, Homophily, and Selection into Treatment," Papers 2209.14391, arXiv.org.
    8. Aifen Feng & Xiaogai Chang & Jingya Fan & Zhengfen Jin, 2023. "Application of LADMM and As-LADMM for a High-Dimensional Partially Linear Model," Mathematics, MDPI, vol. 11(19), pages 1-14, October.
    9. Aifen Feng & Xiaogai Chang & Youlin Shang & Jingya Fan, 2022. "Application of the ADMM Algorithm for a High-Dimensional Partially Linear Model," Mathematics, MDPI, vol. 10(24), pages 1-13, December.
    10. Michael P. Leung & Pantelis Loupos, 2022. "Graph Neural Networks for Causal Inference Under Network Confounding," Papers 2211.07823, arXiv.org, revised Mar 2024.

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