Mortality Forecasting using Factor Models: Time-varying or Time-invariant Factor Loadings?
因子模型的死亡率预测：时变还是固定因子载荷？AI 解读 ↓
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因子模型的死亡率预测：时变还是固定因子载荷？
- 研究问题
- 死亡率因子模型中的年龄载荷若随时间变化，如何改善不同预测期限的表现，并确定短期与长期预测方法的分界？
- 方法与思路
- 作者将经典死亡率因子模型扩展为时变载荷模型，分别用局部回归与朴素方法外推载荷，并以变点分析估计期限分界。
- 材料陈述的贡献
- 摘要称新模型能捕捉载荷变化，并在不同国家和期限改善死亡率预测；另提出分界估计方法，提供模拟考察。
- 阅读关注点
- 应核对时变载荷的估计与识别、两种外推规则、分界选择是否使用未来信息，以及跨国家预测比较的评价设计。
- 阅读边界
- 所据为 arXiv 预印本，不能据此确认同行评审或期刊接收；摘要未列国家、误差数值和完整设定，改进幅度无法判断。
原文摘要
Many existing mortality models follow the framework of classical factor models, such as the Lee-Carter model and its variants. Latent common factors in factor models are defined as time-related mortality indices (such as $κ_t$ in the Lee-Carter model). Factor loadings, which capture the linear relationship between age variables and latent common factors (such as $β_x$ in the Lee-Carter model), are assumed to be time-invariant in the classical framework. This assumption is usually too restrictive in reality as mortality datasets typically span a long period of time. Driving forces such as medical improvement of certain diseases, environmental changes and technological progress may significantly influence the relationship of different variables. In this paper, we first develop a factor model with time-varying factor loadings (time-varying factor model) as an extension of the classical factor model for mortality modelling. Two forecasting methods to extrapolate the factor loadings, the local regression method and the naive method, are proposed for the time-varying factor model. From the empirical data analysis, we find that the new model can capture the empirical feature of time-varying factor loadings and improve mortality forecasting over different horizons and countries. Further, we propose a novel approach based on change point analysis to estimate the optimal `boundary' between short-term and long-term forecasting, which is favoured by the local linear regression and naive method, respectively. Additionally, simulation studies are provided to show the performance of the time-varying factor model under various scenarios.