Unit Averaging for Heterogeneous Panels

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.

本文提出一种单元平均方法，以在异质性面板模型中高效估计各单元的专属参数。该方法通过利用面板中所有单元专属参数估计量的加权平均，对指定单元的参数进行估计。该加权平均的权重通过最小化本文推导得到的均方误差（Mean Squared Error, MSE）准则确定。本文基于频率学派模型平均领域的相关文献启发的局部异质性框架，分析所得最小MSE单元平均估计量的性质，并推导该估计量及其对应权重的局部渐近分布。最后，本文将该方法应用于德国区域面板的失业率预测任务，以此展示其应用优势。