A NEURAL-NETWORK ANALYZER FOR MORTALITY FORECAST
A NEURAL-NETWORK ANALYZER FOR MORTALITY FORECAST
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DOI：
10.1017/asb.2017.45
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发表时间：
2018-05-01
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通讯作者：
Hainaut, Donatien
中科院分区：
文献类型：
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作者：
Hainaut, Donatien
本文提出了一种神经网络方法来预测和模拟人类死亡率。这种半参数模型能够检测和复制在死亡率对数力演化中观察到的非线性。该方法分两步进行。在第一阶段，基于神经网络的主成分分析泛化总结了对数死亡率表面在少数潜在因素中所携带的信息。第二步，利用计量经济模型对这些潜在因素进行预测。其次，对死亡率对数力的期限结构进行逆变换重构。神经分析仪根据1946年至2000年期间法国、英国和美国的死亡率进行调整，并使用2001年至2014年的数据进行验证。数值实验表明，与有无队列效应的Lee-Carter模型相比，该方法具有较好的预测能力。
This article proposes a neural-network approach to predict and simulate human mortality rates. This semi-parametric model is capable to detect and duplicate non-linearities observed in the evolution of log-forces of mortality. The method proceeds in two steps. During the first stage, a neural-network-based generalization of the principal component analysis summarizes the information carried by the surface of log-mortality rates in a small number of latent factors. In the second step, these latent factors are forecast with an econometric model. The term structure of log-forces of mortality is next reconstructed by an inverse transformation. The neural analyzer is adjusted to French, UK and US mortality rates, over the period 1946-2000 and validated with data from 2001 to 2014. Numerical experiments reveal that the neural approach has an excellent predictive power, compared to the Lee-Carter model with and without cohort effects.