论文标题
在汇总辅助数据下使用混合效果随机森林分析机会成本的工作成本
Analysing Opportunity Cost of Care Work using Mixed Effects Random Forests under Aggregated Auxiliary Data
论文作者
论文摘要
基于证据的决策需要可靠的，空间分类的指标。混合效应的框架随机森林在小面积估计中利用随机森林和分层数据的优势。这些方法通常需要访问有关人群级别的辅助信息，这对从业者来说是一个很大的限制。相比之下，我们提出的方法 - 用于点和不确定性估计 - 弃权访问单位级人口数据，但通过校准量调节自适应地纳入了汇总的辅助信息。我们展示了它从社会经济小组和人口普查汇总的德国估算医疗工作成本的用途。仿真研究评估了我们提出的方法。
Evidence-based policy-making requires reliable, spatially disaggregated indicators. The framework of mixed effects random forests leverages the advantages of random forests and hierarchical data in small area estimation. These methods require typically access to auxiliary information on population-level, which is a strong limitation for practitioners. In contrast, our proposed method - for point and uncertainty estimation - abstains from access to unitlevel population data but adaptively incorporates aggregated auxiliary information through calibration-weights. We demonstrate its usage for estimating opportunity cost of care work for Germany from the Socio-Economic Panel and census aggregates. Simulation studies evaluate our proposed method.
