Retiree mortality forecasting: A partial age-range or a full age-range model?

Han Lin Shang, Steven Haberman

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
5 Downloads (Pure)


An essential input of annuity pricing is the future retiree mortality. From observed age-specific mortality data, modeling and forecasting can take place in two routes. On the one hand, we can first truncate the available data to retiree ages and then produce mortality forecasts based on a partial age-range model. On the other hand, with all available data, we can first apply a full age-range model to produce forecasts and then truncate the mortality forecasts to retiree ages. We investigate the difference in modeling the logarithmic transformation of the central mortality rates between a partial age-range and a full age-range model, using data from mainly developed countries in the Human Mortality Database (2020). By evaluating and comparing the short-term point and interval forecast accuracies, we recommend the first strategy by truncating all available data to retiree ages and then produce mortality forecasts. However, when considering the long-term forecasts, it is unclear which strategy is better since it is more difficult to find a model and parameters that are optimal. This is a disadvantage of using methods based on time-series extrapolation for long-term forecasting. Instead, an expectation approach, in which experts set a future target, could be considered, noting that this method has also had limited success in the past.
Original languageEnglish
Article number69
Pages (from-to)1-11
Number of pages11
Issue number3
Publication statusPublished - Sept 2020

Bibliographical note

Copyright 2020 by the authors. Licensee MDPI, Basel, Switzerland. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • age-period-cohort
  • Lee–Carter model with Poisson error
  • Lee–Carter model with Gaussian error
  • Plat model


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