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Projecting UK mortality by using Bayesian generalized additive models

机译：使用贝叶斯广义加性模型预测英国死亡率

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Forecasts of mortality provide vital information about future populations, with implications for pension and healthcare policy as well as for decisions made by private companies about life insurance and annuity pricing. The paper presents a Bayesian approach to the forecasting of mortality that jointly estimates a generalized additive model (GAM) for mortality for the majority of the age range and a parametric model for older ages where the data are sparser. The GAM allows smooth components to be estimated for age, cohort and age-specific improvement rates, together with a non-smoothed period effect. Forecasts for the UK are produced by using data from the human mortality database spanning the period 1961-2013. A metric that approximates predictive accuracy is used to estimate weights for the 'stacking' of forecasts from models with different points of transition between the GAM and parametric elements. Mortality for males and females is estimated separately at first, but a joint model allows the asymptotic limit of mortality at old ages to be shared between sexes and furthermore provides for forecasts accounting for correlations in period innovations.
机译：死亡率预测提供了有关未来人口的重要信息，对养老金和医疗保健政策以及私人公司关于人寿保险和年金定价的决策具有影响。本文提出了一种贝叶斯方法进行死亡率预测，该方法联合估计了大多数年龄范围内的死亡率的广义加性模型（GAM）和数据较稀疏的老年人的参数模型。 GAM允许估算平滑组件的年龄，队列和特定年龄段的改善率，以及不平滑的时期效应。英国的预测是使用1961-2013年期间的人类死亡率数据库中的数据得出的。近似于预测精度的度量标准可用于从GAM和参数元素之间具有不同转换点的模型中估算“堆叠”预测的权重。首先，男性和女性的死亡率是分别估算的，但是联合模型允许在性别之间共享老年死亡率的渐进极限，并且还提供了预测时期创新相关性的预测。

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