Inference in Models of Discrete Choice with Social Interactions Using Network Data

TL;DR

This paper develops theoretical foundations for inference in discrete choice models with social interactions on large networks, validating the use of network HAC variance estimators and providing new central limit theorems.

Contribution

It introduces new CLTs for network moments, justifies network HAC estimators, and applies these results to social interaction models on large networks.

Findings

Network HAC estimators are justified for large network data.

New CLTs for network moments in social interaction models.

Empirical and simulation studies validate theoretical results.

Abstract

This paper studies inference in models of discrete choice with social interactions when the data consists of a single large network. We provide theoretical justification for the use of spatial and network HAC variance estimators in applied work, the latter constructed by using network path distance in place of spatial distance. Toward this end, we prove new central limit theorems for network moments in a large class of social interactions models. The results are applicable to discrete games on networks and dynamic models where social interactions enter through lagged dependent variables. We illustrate our results in an empirical application and simulation study.