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Dynamic modelling of mortality via mixtures of skewed distribution functions

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  • Emanuele Aliverti
  • Stefano Mazzuco
  • Bruno Scarpa

Abstract

There has been growing interest on forecasting mortality. In this article, we propose a novel dynamic Bayesian approach for modelling and forecasting the age‐at‐death distribution, focusing on a three‐component mixture of a Dirac mass, a Gaussian distribution and a skew‐normal distribution. According to the specified model, the age‐at‐death distribution is characterized via seven parameters corresponding to the main aspects of infant, adult and old‐age mortality. The proposed approach focuses on coherent modelling of multiple countries, and following a Bayesian approach to inference we allow to borrow information across populations and to shrink parameters towards a common mean level, implicitly penalizing diverging scenarios. Dynamic modelling across years is induced through an hierarchical dynamic prior distribution that allows to characterize the temporal evolution of each mortality component and to forecast the age‐at‐death distribution. Empirical results on multiple countries indicate that the proposed approach outperforms popular methods for forecasting mortality, providing interpretable insights on its evolution.

Suggested Citation

  • Emanuele Aliverti & Stefano Mazzuco & Bruno Scarpa, 2022. "Dynamic modelling of mortality via mixtures of skewed distribution functions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1030-1048, July.
    • Handle: RePEc:bla:jorssa:v:185:y:2022:i:3:p:1030-1048
    DOI: 10.1111/rssa.12808
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    References listed on IDEAS

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    1. Jackie Li, 2013. "A Poisson common factor model for projecting mortality and life expectancy jointly for females and males," Population Studies, Taylor & Francis Journals, vol. 67(1), pages 111-126, March.
    2. Nan Li & Ronald Lee, 2005. "Coherent mortality forecasts for a group of populations: An extension of the lee-carter method," Demography, Springer;Population Association of America (PAA), vol. 42(3), pages 575-594, August.
    3. Siu Cheung & Jean-Marie Robine & Edward Tu & Graziella Caselli, 2005. "Three dimensions of the survival curve: horizontalization, verticalization, and longevity extension," Demography, Springer;Population Association of America (PAA), vol. 42(2), pages 243-258, May.
    4. Marie-Pier Bergeron-Boucher & Vladimir Canudas-Romo & James E. Oeppen & James W. Vaupel, 2017. "Coherent forecasts of mortality with compositional data analysis," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 37(17), pages 527-566.
    5. Rob Hyndman & Heather Booth & Farah Yasmeen, 2013. "Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models," Demography, Springer;Population Association of America (PAA), vol. 50(1), pages 261-283, February.
    6. Hyndman, Rob J. & Shahid Ullah, Md., 2007. "Robust forecasting of mortality and fertility rates: A functional data approach," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4942-4956, June.
    7. Patefield, Mike, 2000. "Fast and Accurate Calculation of Owen's T Function," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 5(i05).
    8. Stefano Mazzuco & Bruno Scarpa & Lucia Zanotto, 2018. "A mortality model based on a mixture distribution function," Population Studies, Taylor & Francis Journals, vol. 72(2), pages 191-200, May.
    9. Petros Dellaportas & Adrian F. M. Smith & Photis Stavropoulos, 2001. "Bayesian analysis of mortality data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(2), pages 275-291.
    10. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
    11. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
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    Cited by:

    1. Matteo Dimai & Marek Brabec, 2024. "A Bayesian model for age at death with cohort effects," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 51(33), pages 1017-1058.

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