A Structural Econometric Analysis of Network Formation Games Through Subnetworks

• 成果类型: Article
• 署名作者: Sheng, Shuyang
• 署名单位: University of California System; University of California Los Angeles
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.3982/ECTA12558
• 发表日期: 2020
• 页码: 1829-1858
• 关键词: moment inequalities; discrete response; social networks; inference; MODEL; equilibrium; identification; EVOLUTION; regions

摘要

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.