论文标题
在异质代理之间进行社会和经济网络形成中战略互动的最佳测试
An optimal test for strategic interaction in social and economic network formation between heterogeneous agents
论文作者
论文摘要
考虑一个$ n $ players(分为$ k $可观察类型)的设置,形成了一个定向网络。代理对网络形式的偏好由任意网络福利函数组成(例如,代理可能对网络中心性具有偏好)和一个私人组件,该组件可以在自己的链接中加在一起可分离。后一个组件允许在跨代理(分别跨越和内部异质性)以及在$ K $类型的代理商中发送和同质性/异质的代理商(分别跨越和内部异质性)发送和接收链接的成本未观察到的异质性。相比之下,网络福利函数允许代理对与与网络其他地方的链接的链接的偏好(因此与同行的链接形成行为)不同。在排除网络福利函数的零模型中,以\ cite {charbonneau_ej17}描述的方式独立于二元组形式。在替代方案下,在链接决策(即战略互动)之间存在相互依存关系。我们展示了如何用特定方向优化的功率测试零。这些替代方向包括许多战略网络形成的常见模型(例如“连接”模型,“结构孔”模型等)。我们的随机效用规范在零下诱导了指数式的家庭结构,我们利用该结构构建类似的测试,该测试准确地控制了大小(尽管零是一个复合材料,具有许多滋扰参数)。我们进一步展示了如何为特定替代方案构建本地最佳测试,而无需对均衡选择做出任何假设。为了使我们的测试可行,我们引入了一种新的MCMC算法,以模拟我们的测试统计数据的无分布。
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 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 \cite{Charbonneau_EJ17}. 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.