Premium
A Structural Econometric Analysis of Network Formation Games Through Subnetworks
Author(s) -
Sheng Shuyang
Publication year - 2020
Publication title -
econometrica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 16.7
H-Index - 199
eISSN - 1468-0262
pISSN - 0012-9682
DOI - 10.3982/ecta12558
Subject(s) - subnetwork , computer science , network formation , selection (genetic algorithm) , identification (biology) , network structure , mathematical optimization , equilibrium selection , monte carlo method , game theory , mathematical economics , theoretical computer science , mathematics , artificial intelligence , repeated game , statistics , botany , computer security , world wide web , biology
The objective of this paper is to identify and estimate network formation models using observed data on network structure. We characterize network formation as a simultaneous‐move game, where the utility from forming a link depends on the structure of the network, thereby generating strategic interactions between links. With the prevalence of multiple equilibria, the parameters are not necessarily point identified. We leave the equilibrium selection unrestricted and propose a partial identification approach. We derive bounds on the probability of observing a subnetwork, where a subnetwork is the restriction of a network to a subset of the individuals. Unlike the standard bounds as in Ciliberto and Tamer (2009), these subnetwork bounds are computationally tractable in large networks provided we consider small subnetworks. We provide Monte Carlo evidence that bounds from small subnetworks are informative in large networks.