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

Eric Auerbach

arXiv 22 Mar 2019 · Econometrics · publishedEconometrica (2022) · 45 citations (OpenAlex)

arXiv:1903.09679 · PDF · DOI · OpenAlex · Extracted main text

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. Auerbach (2021) considers inference and an application to network peer effects.

Citation extraction

21
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distinct cited
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Auerbach, E (2021) Identification and estimation of a partially linear regression model using network data: Inference and an application to network… self1.00063100%
2Graham, B. S (2019) Network data0.6443267%
3Arduini, T., E. Patacchini, and E. Rainone (2015) Parametric and semiparametric iv estimation of network models with selectivity0.64422100%
4Johnsson, I. and H. R. Moon (2015) Estimation of peer effects in endogenous social networks: Control function approach0.64422100%
5Ahn, H. and J. L. Powell (1993) Semiparametric estimation of censored selection models with a nonparametric selection mechanism0.5112250%
6Bramoullé, Y., H. Djebbari, and B. Fortin (2009) Identification of peer effects through social networks0.40511100%
7Bramoullé, Y., H. Djebbari, and B. Fortin (2019) Peer effects in networks: A survey0.40511100%
8de Giorgi, G., M. Pellizzari, and S. Redaelli (2010) Identification of social interactions through partially overlapping peer groups0.40511100%
9de Paula, Á (2020) Econometric models of network formation0.40511100%
10Ferraty, F. and P. Vieu (2006) Nonparametric functional data analysis: theory and practice0.40511100%

Showing the top 10 of 21 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
11 Identification and Estimation of a Partially Linear Regression Model using Network Data: Inference and an Application to Network Peer Effects1.00093
2Identification and Estimation of a Semiparametric Logit Model using Network Data1.00063
3Semiparametric Dynamic Logit Model with Endogenous Networks1.00054
4The Network Propensity Score: Spillovers, Homophily, and Selection into Treatment0.73732
5Set-Valued Control Functions0.73732
6Improving control over unobservables with network data0.73732
7Learning Dependence Structures for Econometric Inference0.64432
8Social Interactions in Endogenous Groups0.64422
9Endogenous Interference in Randomized Experiments0.64422
10Spectral estimation of large stochastic blockmodels with discrete nodal covariates0.51121