Unit Averaging for Heterogeneous Panels
提出一种单位平均化方法，通过加权平均所有单位的参数估计来高效恢复异质性面板中每个单位的特定参数，权重由最小化均方误差准则确定，并用于预测德国各地区的失业率。
In this work we introduce a unit averaging procedure to efficiently recover unit-specific parameters in a heterogeneous panel model. The procedure consists in estimating the parameter of a given unit using a weighted average of all the unit-specific parameter estimators in the panel. The weights of the average are determined by minimizing an MSE criterion we derive. We analyze the properties of the resulting minimum MSE unit averaging estimator in a local heterogeneity framework inspired by the literature on frequentist model averaging, and we derive the local asymptotic distribution of the estimator and the corresponding weights. The benefits of the procedure are showcased with an application to forecasting unemployment rates for a panel of German regions.