An Optimal Test for Strategic Interaction in Network Formation Games

• 成果类型: Article; Early Access
• 署名作者: Pelican, Andrin; Graham, Bryan S.
• 署名单位: University of California System; University of California Berkeley; National Bureau of Economic Research
• 刊物名称: REVIEW OF ECONOMIC STUDIES
• ISSN/ISSBN: 0034-6527
• DOI: 10.1093/restud/rdag089
• 发表日期: 2026
• 关键词: Random graphs; MODEL; identification; inference

摘要

Consider a setting where N players, partitioned into K observable types, form a directed network. Agents' preferences over the form of the network consist of an arbitrary network benefit function (e.g. agents may have preferences over their network centrality) and a private, or dyadic, component which is additively separable in own links. This latter component allows for unobserved heterogeneity in the costs of sending and receiving links across agents (respectively out- and in- degree heterogeneity) as well as homophily/heterophily across the K types of agents. In contrast, the network benefit function allows agents' preferences over links to vary with the presence or absence of links elsewhere in the network (and hence with the link formation behavior of their peers). In the null model, which excludes the network benefit function, links form independently across dyads in the manner described by Charbonneau, 2017, Econometrics Journal, 20, S1-S13 among others. Under the alternative, there is interdependence across linking decisions (i.e. strategic interaction). We show how to test the null with power optimized in specific directions. These alternative directions include many common models of strategic network formation (e.g. connections models, structural hole models etc.). Our random utility specification induces an exponential family structure under the null which we exploit to construct a similar test which exactly controls size (despite the the null being a composite one with many nuisance parameters). We further show how to construct locally best tests for specific alternatives without making any assumptions about equilibrium selection. To make our tests feasible, we introduce a new MCMC algorithm for simulating the null distributions of our test statistics.