{
  "annotation_id": "f73de04d-3f39-4f15-b855-57a96ef7e518",
  "date_created": "2026-02-17T05:53:04.272000Z",
  "date_modified": "2026-02-17T05:53:04.272000Z",
  "file_hash": "222204b59fe85b26314035f33817607f9dc6f7610c0c9ded936f8e82e7607ca4",
  "private": false,
  "record": {
    "abstract": "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.\n  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.",
    "arxiv_id": "2601.05702",
    "authors": [
      "Runze Li",
      "Rui Zhou",
      "David Pitt"
    ],
    "categories": [
      "econ.EM"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Dynamic Mortality Forecasting via Mixed-Frequency State-Space Models",
    "url": "https://arxiv.org/abs/2601.05702",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "33c1a0cf-fe73-4633-aeac-040f627e6167",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}