Dynamic Mortality Forecasting via Mixed-Frequency State-Space Models
通过混合频率状态空间模型进行动态死亡率预测AI 解读 ↓
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通过混合频率状态空间模型进行动态死亡率预测
- 研究问题
- 如何联合利用年度死亡率与月度死亡人数，在年度总量公布前更新死亡率状态，并改善年内即时预测和多年预测？
- 方法与思路
- 作者将 Lee–Carter 模型扩展为混合频率状态空间模型，以共享月度因子连接两类数据，结合季节 ARIMA、EM 与卡尔曼方法估计。
- 材料陈述的贡献
- 摘要称，美国样本中汇总月度预测通常更准确，月度信息改善年度即时预测，且频率协调对联合模型的额外帮助有限。
- 阅读关注点
- 应核对月度人数与年度率的观测方程、人口暴露量处理、预测窗口和区间覆盖，以及固定参数更新在结构变化下的表现。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；结论来自所述美国样本，摘要未给误差数值或区间校准结果。
原文摘要
High-frequency death counts are now widely available and contain timely information about intra-year mortality dynamics, but most stochastic mortality models are still estimated on annual data and therefore update only when annual totals are released. We propose a mixed-frequency state-space (MF--SS) extension of the Lee--Carter framework that jointly uses annual mortality rates and monthly death counts. The two series are linked through a shared latent monthly mortality factor, with the annual period factor defined as the intra-year average of the monthly factors. The latent monthly factor follows a seasonal ARIMA process, and parameters are estimated by maximum likelihood using an EM algorithm with Kalman filtering and smoothing. This setup enables real-time intra-year updates of the latent state and forecasts as new monthly observations arrive without re-estimating model parameters. Using U.S. data for ages 20--90 over 1999--2019, we evaluate intra-year annual nowcasts and one- to five-year-ahead forecasts. The MF--SS model produces both a direct annual forecast and an annual forecast implied by aggregating monthly projections. In our application, the aggregated monthly forecast is typically more accurate. Incorporating monthly information substantially improves intra-year annual nowcasts, especially after the first few months of the year. As a benchmark, we also fit separate annual and monthly Lee--Carter models and combine their forecasts using temporal reconciliation. Reconciliation improves these independent forecasts but adds little to MF--SS forecasts, consistent with MF--SS pooling information across frequencies during estimation. The MF--SS aggregated monthly forecasts generally outperform both unreconciled and temporally reconciled Lee--Carter forecasts and produce more cautious predictive intervals than the reconciled Lee--Carter approach.