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

Eric Auerbach*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)347-365
Number of pages19
JournalEconometrica
Volume90
Issue number1
DOIs
StatePublished - Jan 2022

ASJC Scopus subject areas

  • Economics and Econometrics

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