Two-Step Estimation of Network-Formation Models with Incomplete Information

39 Pages Posted: 21 Apr 2013 Last revised: 27 Jun 2015

See all articles by Michael P. Leung

Michael P. Leung

University of Southern California - Department of Economics

Date Written: June 26, 2015

Abstract

We model network formation as a simultaneous game of incomplete information, allowing linking decisions to depend on the structure of the network as well as the attributes of agents. When the data is rationalized by a symmetric equilibrium, meaning observationally equivalent agents choose the same ex-ante strategies, the model can be estimated using a computationally simple two-step estimator. We derive its asymptotic properties under a sequence of models sending the number of agents to infinity, which enables inference with only a single network observation. Our procedure generalizes dyadic regression, allowing the latent index to be a function of endogenous regressors that depend on the network. We apply the estimator to study trust networks in rural Indian villages.

Keywords: social networks, network formation, multiple equilibria, large-market asymptotics, discrete games of incomplete information

JEL Classification: C13, C31, D85

Suggested Citation

Leung, Michael, Two-Step Estimation of Network-Formation Models with Incomplete Information (June 26, 2015). Available at SSRN: https://ssrn.com/abstract=2254145 or http://dx.doi.org/10.2139/ssrn.2254145

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

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