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

109 Pages Posted: 7 Sep 2019 Last revised: 17 Oct 2019

See all articles by Michael P. Leung

Michael P. Leung

University of Southern California - Department of Economics

Date Written: September 2, 2019

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.

Keywords: social networks, peer effects, empirical games, HAC estimator

JEL Classification: C22, C31, C57

Suggested Citation

Leung, Michael, Inference in Models of Discrete Choice with Social Interactions Using Network Data (September 2, 2019). Available at SSRN: https://ssrn.com/abstract=3446926 or http://dx.doi.org/10.2139/ssrn.3446926

Michael Leung (Contact Author)

University of Southern California - Department of Economics ( email )

3620 South Vermont Ave.
Kaprielian (KAP) Hall, 310A
Los Angeles, CA 90089
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
98
Abstract Views
523
Rank
505,895
PlumX Metrics