To pool or not to pool: What is a good strategy for parameter estimation and forecasting in panel regressions?
提出一种新的最优合并平均估计量，在合并的效率提升与异质性导致的偏差之间权衡，通过理论和数值比较发现该估计量在非极端情形下表现优越，为面板数据回归中的估计和预测提供实用指导。
Summary This paper considers estimating the slope parameters and forecasting in potentially heterogeneous panel data regressions with a long time dimension. We propose a novel optimal pooling averaging estimator that makes an explicit trade‐off between efficiency gains from pooling and bias due to heterogeneity. By theoretically and numerically comparing various estimators, we find that a uniformly best estimator does not exist and that our new estimator is superior in nonextreme cases and robust in extreme cases. Our results provide practical guidance for the best estimator and forecast depending on features of data and models. We apply our method to examine the determinants of sovereign credit default swap spreads and forecast future spreads.