Getting the right tail right: Modeling tails of health expenditure distributions
针对健康支出数据中的极端值导致传统最小二乘法偏误的问题，提出一种恢复分布右尾的三部分模型，应用于德国私人健康保险数据，发现年龄梯度估计与标准方法显著不同。
Health expenditure data almost always include extreme values, implying that the underlying distribution has heavy tails. This may result in infinite variances as well as higher-order moments and bias the commonly used least squares methods. To accommodate extreme values, we propose an estimation method that recovers the right tail of health expenditure distributions. It extends the popular two-part model to develop a novel three-part model. We apply the proposed method to claims data from one of the biggest German private health insurers. Our findings show that the estimated age gradient in health care spending differs substantially from the standard least squares method.