Optimal Shrinkage Estimation of Fixed Effects in Linear Panel Data Models
提出一种在收缩估计量类中达到最优均方误差的固定效应估计方法，无需分布假设，允许固定效应随时间变化且存在序列相关，并给出向前一期预测方法。
Shrinkage methods are frequently used to improve the precision of least squares estimators of fixed effects. However, widely used shrinkage estimators guarantee improved precision only under strong distributional assumptions. I develop an estimator for the fixed effects that obtains the best possible mean squared error within a class of shrinkage estimators. This class includes conventional shrinkage estimators and the optimality does not require distributional assumptions. The estimator has an intuitive form and is easy to implement. Moreover, the fixed effects are allowed to vary with time and to be serially correlated, in which case the shrinkage optimally incorporates the underlying correlation structure. I also provide a method to forecast fixed effects one period ahead in this setting.