Identification and Estimation of a Partially Linear Regression Model Using Network Data
成果类型：
Article
署名作者：
Auerbach, Eric
署名单位：
Northwestern University
刊物名称：
ECONOMETRICA
ISSN/ISSBN：
0012-9682
DOI：
10.3982/ECTA19794
发表日期：
2022
页码：
347-365
关键词：
摘要：
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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