Identification and Estimation of a Partially Linear Regression Model Using Network Data
研究一个回归模型中某个协变量是网络连接形成的潜在驱动因素的未知函数，提出基于平方邻接矩阵列匹配的新方法，并给出参数的一致估计量。
I study a regression model in which one covariate is an unknown function of a latent driver of link formation in a network. Rather than specify and fit a parametric network formation model, I introduce a new method based on matching pairs of agents with similar columns of the squared adjacency matrix, the ij th entry of which contains the number of other agents linked to both agents i and j . The intuition behind this approach is that for a large class of network formation models the columns of the squared adjacency matrix characterize all of the identifiable information about individual linking behavior. In this paper, I describe the model, formalize this intuition, and provide consistent estimators for the parameters of the regression model.