Getting the Right Tail Right: Modeling tails of health expenditure distributions - Martin Karlsson; Yulong Wang; Nicolas R. Ziebarth (ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung, 2023)
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.
Keywords: C10; C13; I10; I13; 330; heavy tails; health expenditures; claims data; nonlinear model