B0239
Title: Network regression and supervised centrality estimation
Authors: Junhui Jeffrey Cai - University of Notre Dame (United States) [presenting]
Dan Yang - University of Hong Kong (Hong Kong)
Wu Zhu - Tsinghua University (China)
Haipeng Shen - The University of Hong Kong (Hong Kong)
Linda Zhao - University of Pennsylvania (United States)
Abstract: 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 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.
