  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.
