论文标题
退休人员死亡率预测：部分年龄段还是全年龄段的模型？
Retiree mortality forecasting: A partial age-range or a full age-range model?
论文作者
论文摘要
年金定价的基本输入是未来的退休人员死亡率。从观察到的年龄特异性死亡率数据中，可以在两条途径中进行建模和预测。一方面，我们可以首先将可用数据截断为退休人员年龄，然后基于部分年龄段模型产生死亡率预测。另一方面，有了所有可用的数据，我们可以首先采用全年龄段模型来产生预测，然后将死亡率预测截断为退休人员年龄。我们使用人类死亡率数据库中主要发达国家（2020）中主要发达国家（2020）中主要发达国家（2020年）的数据来研究建模部分年龄段和全年龄段模型之间中央死亡率之间的对数转换的差异。通过评估和比较短期点和间隔预测精度，我们建议通过将所有可用数据截断为退休人员年龄，然后产生死亡率预测，建议您进行第一种策略。但是，在考虑长期预测时，尚不清楚哪种策略更好，因为很难找到最佳的模型和参数。这是使用基于时间序列外推的方法进行长期预测的缺点。取而代之的是，可以考虑一种期望方法，其中专家设定了未来的目标，并指出该方法过去的成功也有限。
An essential input of annuity pricing is the future retiree mortality. From observed age-specific mortality data, modeling and forecasting can be taken place in two routes. On the one hand, we can first truncate the available data to retiree ages and then produce mortality forecasts based on a partial age-range model. On the other hand, with all available data, we can first apply a full age-range model to produce forecasts and then truncate the mortality forecasts to retiree ages. We investigate the difference in modeling the logarithmic transformation of the central mortality rates between a partial age-range and a full age-range model, using data from mainly developed countries in the Human Mortality Database (2020). By evaluating and comparing the short-term point and interval forecast accuracies, we recommend the first strategy by truncating all available data to retiree ages and then produce mortality forecasts. However, when considering the long-term forecasts, it is unclear which strategy is better since it is more difficult to find a model and parameters that are optimal. This is a disadvantage of using methods based on time series extrapolation for long-term forecasting. Instead, an expectation approach, in which experts set a future target, could be considered, noting that this method has also had limited success in the past.
