The centrality in a network is a popular metric for agents' network positions and is often used in regression models to model the network effect on an outcome variable of interest. In empirical studies, researchers often adopt a two-stage procedure to first estimate the centrality and then infer the network effect using the estimated centrality. Despite its prevalent adoption, this two-stage procedure lacks theoretical backing and can fail in both estimation and inference. We, therefore, propose a unified framework, under which we prove the shortcomings of the two-stage in centrality estimation and the undesirable consequences in the regression. We then propose a novel supervised network centrality estimation (SuperCENT) methodology that simultaneously yields superior estimations of the centrality and the network effect and provides valid and narrower confidence intervals than those from the two-stage. We showcase the superiority of SuperCENT in predicting the currency risk premium based on the global trade network.
翻译:在经验研究中,研究人员往往采取两阶段程序,首先对中心作用作出估计,然后用估计中心作用来推断网络效应。尽管这一两阶段程序普遍采用,但缺乏理论支持,在估计和推论两方面都可能失败。因此,我们提出了一个统一框架,根据这个框架,我们证明核心估计两个阶段的缺点和倒退的不良后果。然后我们提出一个新的、受监督的网络中心作用估计方法,同时得出对中心作用和网络效应的优估,并提供与两阶段相比的有效和狭窄的信任间隔。我们展示超能力公司在预测基于全球贸易网络的货币风险溢价方面的优势。