Identification and Estimation of a Partially Linear Regression Model using Network Data: Inference and an Application to Network Peer Effects

TL;DR

This paper extends the analysis of partially linear regression models with network data, focusing on estimator properties, model extensions, and an application to peer effects, supported by simulations.

Contribution

It provides new large sample results, model extensions, and an application to network peer effects, building on and extending Auerbach (2019a).

Findings

Estimator properties are established for large samples.

Model extensions improve flexibility and applicability.

Simulation results validate the proposed methods.

Abstract

This paper provides additional results relevant to the setting, model, and estimators of Auerbach (2019a). Section 1 contains results about the large sample properties of the estimators from Section 2 of Auerbach (2019a). Section 2 considers some extensions to the model. Section 3 provides an application to estimating network peer effects. Section 4 shows the results from some simulations.