Fixed‐Effect Regressions on Network Data
研究了从网络数据估计线性回归模型中固定效应的推断问题，推导了网络结构对估计精度的影响，并给出了一致估计和有效推断的充分条件，适用于雇主-雇员或学生-教师等配对数据。
This paper considers inference on fixed effects in a linear regression model estimated from network data. An important special case of our setup is the two‐way regression model. This is a workhorse technique in the analysis of matched data sets, such as employer–employee or student–teacher panel data. We formalize how the structure of the network affects the accuracy with which the fixed effects can be estimated. This allows us to derive sufficient conditions on the network for consistent estimation and asymptotically valid inference to be possible. Estimation of moments is also considered. We allow for general networks and our setup covers both the dense and the sparse case. We provide numerical results for the estimation of teacher value‐added models and regressions with occupational dummies.