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Mortality data correction in the absence of monthly fertility records

Author

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  • Alexandre Boumezoued

    (R&D, Milliman, Paris - Milliman France)

  • Amal Elfassihi

    (R&D, Milliman, Paris - Milliman France)

Abstract

Since the conjecture of Richards (2008), the work by Cairns et al. (2016) and subsequent developments by Boumezoued (2016), Boumezoued et al. (2018) and Boumezoued et al. (2019), it has been acknowledged that observations from censuses have led to major problems of reliability in estimates of general population mortality rates as implemented in practice. These issues led to misinterpretation of some key mortality characteristics in the past decades, including "false cohort effects". To overcome these issues, the exposure estimates for a given country can be corrected by using monthly fertility records. However, in the absence of birth-by-month data, the recent developments are not applicable. Therefore, this paper explores new solutions regarding the construction of mortality tables in this context, based on machine learning techniques. As a main result, it is demonstrated that the new exposure models proposed in this paper allow to provide correction with high quality and to improve the fitting of stochastic mortality models without cohort component, as it is the case for the existing correction method based on monthly fertility data.

Suggested Citation

  • Alexandre Boumezoued & Amal Elfassihi, 2020. "Mortality data correction in the absence of monthly fertility records," Working Papers hal-02634631, HAL.
    • Handle: RePEc:hal:wpaper:hal-02634631
    Note: View the original document on HAL open archive server: https://hal.science/hal-02634631
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    References listed on IDEAS

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    1. Andrew J. G. Cairns & David Blake & Kevin Dowd & Amy R. Kessler, 2016. "Phantoms never die: living with unreliable population data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(4), pages 975-1005, October.
    2. Susanna Levantesi & Virginia Pizzorusso, 2019. "Application of Machine Learning to Mortality Modeling and Forecasting," Risks, MDPI, vol. 7(1), pages 1-19, February.
    3. Andrew Cairns & David Blake & Kevin Dowd & Guy Coughlan & David Epstein & Alen Ong & Igor Balevich, 2009. "A Quantitative Comparison of Stochastic Mortality Models Using Data From England and Wales and the United States," North American Actuarial Journal, Taylor & Francis Journals, vol. 13(1), pages 1-35.
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    More about this item

    Keywords

    Human Mortality Database; cohort effect; anomalous mortality data; stochastic mortality models; machine learning; neural network;
    All these keywords.

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