Dynamic Mortality Forecasting via Mixed-Frequency State-Space Models

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.