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A NEW INFERENCE STRATEGY FOR GENERAL POPULATION MORTALITY TABLES

Published online by Cambridge University Press:  17 April 2020

Alexandre Boumezoued*
Affiliation:
Milliman R&D, 14 Avenue de la Grande Armée, 75017Paris, France, E-mail: alexandre.boumezoued@milliman.com
Marc Hoffmann
Affiliation:
CEREMADE, CNRS-UMR 7534, Universite Paris Dauphine, Place du maréchal de Lattre de Tassigny, 75016Paris, France, E-mail: hoffmann@ceremade.dauphine.fr
Paulien Jeunesse
Affiliation:
CEREMADE, CNRS-UMR 7534, Universite Paris Dauphine, Place du maréchal de Lattre de Tassigny, 75016Paris, France, E-mail: jeunesse@ceremade.dauphine.fr

Abstract

We propose a new inference strategy for general population mortality tables based on annual population and death estimates, completed by monthly birth counts. We rely on a deterministic population dynamics model and establish formulas that link the death rates to be estimated with the observables at hand. The inference algorithm takes the form of a recursive and implicit scheme for computing death rate estimates. This paper demonstrates both theoretically and numerically the efficiency of using additional monthly birth counts for appropriately computing annual mortality tables. As a main result, the improved mortality estimators show better features, including the fact that previous anomalies in the form of isolated cohort effects disappear, which confirms from a mathematical perspective the previous contributions by Richards, Cairns et al., and Boumezoued.

Type
Research Article
Copyright
© Astin Bulletin 2020

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