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Bayesian forecasting of mortality rates by using latent Gaussian models

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  • Angelos Alexopoulos
  • Petros Dellaportas
  • Jonathan J. Forster

Abstract

We provide forecasts for mortality rates by using two different approaches. First we employ dynamic non‐linear logistic models based on the Heligman–Pollard formula. Second, we assume that the dynamics of the mortality rates can be modelled through a Gaussian Markov random field. We use efficient Bayesian methods to estimate the parameters and the latent states of the models proposed. Both methodologies are tested with past data and are used to forecast mortality rates both for large (UK and Wales) and small (New Zealand) populations up to 21 years ahead. We demonstrate that predictions for individual survivor functions and other posterior summaries of demographic and actuarial interest are readily obtained. Our results are compared with other competing forecasting methods.

Suggested Citation

  • Angelos Alexopoulos & Petros Dellaportas & Jonathan J. Forster, 2019. "Bayesian forecasting of mortality rates by using latent Gaussian models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(2), pages 689-711, February.
    • Handle: RePEc:bla:jorssa:v:182:y:2019:i:2:p:689-711
    DOI: 10.1111/rssa.12422
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    Cited by:

    1. Ka Kin Lam & Bo Wang, 2021. "Robust Non-Parametric Mortality and Fertility Modelling and Forecasting: Gaussian Process Regression Approaches," Forecasting, MDPI, vol. 3(1), pages 1-21, March.
    2. Wang, Pengjie & Pantelous, Athanasios A. & Vahid, Farshid, 2023. "Multi-population mortality projection: The augmented common factor model with structural breaks," International Journal of Forecasting, Elsevier, vol. 39(1), pages 450-469.
    3. Gisou Díaz-Rojo & Ana Debón & Jaime Mosquera, 2020. "Multivariate Control Chart and Lee–Carter Models to Study Mortality Changes," Mathematics, MDPI, vol. 8(11), pages 1-17, November.

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