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Mortality data correction in the absence of monthly fertility records

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  • Boumezoued, Alexandre
  • Elfassihi, Amal

Abstract

Since the conjecture of Richards (2008), the work by Cairns et al. (2016) and subsequent developments by Boumezoued (2020), Boumezoued et al. (2020) 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 mis-interpretation 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 a cohort component, as it is the case for the existing correction method based on monthly fertility data.

Suggested Citation

  • Boumezoued, Alexandre & Elfassihi, Amal, 2021. "Mortality data correction in the absence of monthly fertility records," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 486-508.
  • Handle: RePEc:eee:insuma:v:99:y:2021:i:c:p:486-508
    DOI: 10.1016/j.insmatheco.2021.03.019
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    References listed on IDEAS

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    1. Haberman, Steven & Renshaw, Arthur, 2011. "A comparative study of parametric mortality projection models," Insurance: Mathematics and Economics, Elsevier, vol. 48(1), pages 35-55, January.
    2. 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.
    3. Boumezoued, Alexandre & Hoffmann, Marc & Jeunesse, Paulien, 2020. "A New Inference Strategy For General Population Mortality Tables," ASTIN Bulletin, Cambridge University Press, vol. 50(2), pages 325-356, May.
    4. Susanna Levantesi & Virginia Pizzorusso, 2019. "Application of Machine Learning to Mortality Modeling and Forecasting," Risks, MDPI, vol. 7(1), pages 1-19, February.
    5. 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.
    6. Plat, Richard, 2009. "On stochastic mortality modeling," Insurance: Mathematics and Economics, Elsevier, vol. 45(3), pages 393-404, December.
    7. Andrew J. G. Cairns & David Blake & Kevin Dowd, 2006. "A Two‐Factor Model for Stochastic Mortality with Parameter Uncertainty: Theory and Calibration," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 73(4), pages 687-718, December.
    8. S. J. Richards, 2008. "Detecting year‐of‐birth mortality patterns with limited data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(1), pages 279-298, January.
    9. Hainaut, Donatien, 2018. "A Neural-Network Analyzer For Mortality Forecast," ASTIN Bulletin, Cambridge University Press, vol. 48(2), pages 481-508, May.
    10. Hainaut, Donatien, 2018. "A Neural-Network Analyzer for Mortality Forecast," LIDAM Reprints ISBA 2018027, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    11. Keyes, Katherine M. & Utz, Rebecca L. & Robinson, Whitney & Li, Guohua, 2010. "What is a cohort effect? Comparison of three statistical methods for modeling cohort effects in obesity prevalence in the United States, 1971-2006," Social Science & Medicine, Elsevier, vol. 70(7), pages 1100-1108, April.
    12. Ludkovski, Mike & Risk, Jimmy & Zail, Howard, 2018. "Gaussian Process Models For Mortality Rates And Improvement Factors," ASTIN Bulletin, Cambridge University Press, vol. 48(3), pages 1307-1347, September.
    13. Renshaw, A.E. & Haberman, S., 2006. "A cohort-based extension to the Lee-Carter model for mortality reduction factors," Insurance: Mathematics and Economics, Elsevier, vol. 38(3), pages 556-570, June.
    14. Ludkovski, Mike & Risk, Jimmy & Zail, Howard, 2018. "Gaussian Process Models For Mortality Rates And Improvement Factors – Corrigendum," ASTIN Bulletin, Cambridge University Press, vol. 48(3), pages 1349-1349, September.
    15. Philippe Deprez & Pavel V. Shevchenko & Mario V. Wuthrich, 2017. "Machine Learning Techniques for Mortality Modeling," Papers 1705.03396, arXiv.org.
    16. 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.
    17. Brouhns, Natacha & Denuit, Michel & Vermunt, Jeroen K., 2002. "A Poisson log-bilinear regression approach to the construction of projected lifetables," Insurance: Mathematics and Economics, Elsevier, vol. 31(3), pages 373-393, December.
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