Mortality data correction in the absence of monthly fertility records

Alexandre Boumezoued () and Amal Elfassihi ()
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Alexandre Boumezoued: R&D, Milliman, Paris - Milliman France
Amal Elfassihi: R&D, Milliman, Paris - Milliman France

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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.

Keywords: Human Mortality Database; cohort effect; anomalous mortality data; stochastic mortality models; machine learning; neural network (search for similar items in EconPapers)
Date: 2020-05-27
New Economics Papers: this item is included in nep-big, nep-hea and nep-ore
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