This paper proposes an asymmetric grouping estimator for forecasting per capita carbon emissions for a country panel. The estimator relies on the observation that a bias‐variance pooling trade‐off in potentially heterogeneous panel data may be different across countries. For a specific country, cross validation is used to determine the optimal country‐specific grouping. A simulated annealing algorithm deals with the combinatorial problem of group selection in large cross sections. A Monte Carlo study shows that in case of heterogenous parameters, the asymmetric grouping estimators outperforms symmetric grouping approaches and forecasting based on individual estimates. Only in the case where the signal is very weak, pooling all countries leads to better forecasting performance. Similar results are found when forecasting carbon emission. The asymmetric grouping estimator leads to more pooling than a symmetric approach. Being on the same continent increases the probability of pooling, and African countries seem to benefit most from using asymmetric grouping and European countries least.

中文翻译：

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使用不对称分组预测碳排放

本文提出了一种非对称分组估计器，用于预测国家面板的人均碳排放量。估计器依赖于这样的观察：潜在异质面板数据中的偏差-方差池权衡可能因国家而异。对于特定国家/地区，交叉验证用于确定最佳的特定国家/地区分组。模拟退火算法处理大横截面中的组选择的组合问题。蒙特卡罗研究表明，在参数异质的情况下，不对称分组估计量优于对称分组方法和基于个体估计的预测。只有在信号非常弱的情况下，汇集所有国家才能带来更好的预测性能。在预测碳排放时也发现了类似的结果。非对称分组估计器比对称方法产生更多的池化。位于同一大陆会增加合并的可能性，非洲国家似乎从使用不对称分组中受益最多，而欧洲国家受益最少。