This paper introduces a small area estimation approach that borrows strength across domains (areas) and time and is efficiently used to obtain labour force estimators by economic activity. Specifically, the data across time are used to select different models for each domain; such selection is done with an aggregated mixed generalized Akaike information criterion statistic which is obtained using data across all time points and then is split into individual component for each domain. The approach makes a selection from different estimators, including the direct estimator, synthetic and mixed estimators derived from different models using auxiliary information. Results from several simulation experiments, some with original designs, show the good performance of the approach against standard small area approaches. In addition, it is shown the important practical advantages in the real application.

本文介绍了一种小面积估算方法，该方法借鉴了各个领域（地区）和时间的优势，可以有效地用于通过经济活动获得劳动力估算值。具体来说，跨时间的数据用于为每个域选择不同的模型；此类选择是使用汇总的混合广义Akaike信息准则统计量完成的，该统计量统计量是使用所有时间点的数据获得的，然后划分为每个域的各个组成部分。该方法从不同的估计量中进行选择，包括直接估计量，使用辅助信息从不同模型得出的综合和混合估计量。几个模拟实验的结果（有些是原始设计的）显示了该方法相对于标准小面积方法的良好性能。此外，