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Modeling old-age mortality risk for the populations of Australia and New Zealand: An extreme value approach

机译:对澳大利亚和新西兰人口的老年死亡风险进行建模:一种极端价值方法

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Old-age mortality for populations of developed countries has been improving rapidly since the 1950s. This phenomenon, which is often referred to as 'rectangularization' of mortality, implies an increased survival at advanced ages. With this increase comes different challenges to actuaries, economists and policy planners. A reliable estimate of old-age mortality would definitely help them develop various demographic and financial projections. Unfortunately, data quality issues have made the modeling of old-age mortality difficult and we need a method that can extrapolate a survival distribution to extreme ages without requiring accurate mortality data for the centenarian population. In this paper, we focus on a method called the threshold life table which systematically integrates extreme value theory to the parametric modeling of mortality. We apply the threshold life table to model the most recent period (static) mortality rates for the populations of Australia and New Zealand. We observe a good fit to the raw data for both populations. We then extend the model to predict the highest attained age, which is commonly referred to as 'omega' or ω in the actuarial literature, for the populations of Australia and New Zealand. On the basis of the threshold life table, the central estimates of ω for Australia and New Zealand are 112.20 and 109.43, respectively. Our estimates of to are reasonably consistent with the validated supercentenarian in these countries.
机译:自1950年代以来,发达国家人口的老年死亡率一直在迅速提高。这种现象通常被称为死亡率的“矩形化”,暗示着高龄者的生存率提高。随之而来的是精算师,经济学家和政策计划者的不同挑战。对老年死亡率的可靠估计肯定会帮助他们制定各种人口和财务预测。不幸的是,数据质量问题使老年死亡率的建模变得困难,我们需要一种无需百岁老人的准确死亡率数据就能将生存分布推断到极端年龄的方法。在本文中,我们集中于一种称为阈值寿命表的方法,该方法将极值理论与死亡率的参数化建模系统地集成在一起。我们使用阈值寿命表来模拟澳大利亚和新西兰人口的最新时期(静态)死亡率。我们发现这两个群体的原始数据都非常合适。然后,我们扩展模型以预测澳大利亚和新西兰人口的最高年龄,在精算文学中通常被称为“ω”或ω。根据阈值寿命表,澳大利亚和新西兰的ω的中心估计分别为112.20和109.43。我们对的估计与这些国家/地区经过验证的超百岁老人合理地一致。

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