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A GROUP REGULARISATION APPROACH FOR CONSTRUCTING GENERALISED AGE-PERIOD-COHORT MORTALITY PROJECTION MODELS

Published online by Cambridge University Press:  09 November 2021

Dilan SriDaran ,
Michael Sherris ,
Andrés M. Villegas  and
Jonathan Ziveyi Open the ORCID record for Jonathan Ziveyi [Opens in a new window]
Dilan SriDaran
Affiliation:
School of Risk and Actuarial Studies and ARC Centre of Excellence in Population Ageing Research (CEPAR) UNSW Sydney Sydney, NSW 2052, Australia E-Mail: dilan.sridaran@gmail.com
Michael Sherris
Affiliation:
School of Risk and Actuarial Studies and ARC Centre of Excellence in Population Ageing Research (CEPAR) UNSW Sydney Sydney, NSW 2052, Australia E-Mail: m.sherris@unsw.edu.au
Andrés M. Villegas*
Affiliation:
School of Risk and Actuarial Studies and ARC Centre of Excellence in Population Ageing Research (CEPAR) UNSW Sydney Sydney, NSW 2052, Australia
Jonathan Ziveyi
Affiliation:
School of Risk and Actuarial Studies and ARC Centre of Excellence in Population Ageing Research (CEPAR) UNSW Sydney Sydney, NSW 2052, Australia E-Mail: j.ziveyi@unsw.edu.au
*
E-Mail: a.villegas@unsw.edu.au
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Abstract

Given the rapid reductions in human mortality observed over recent decades and the uncertainty associated with their future evolution, there have been a large number of mortality projection models proposed by actuaries and demographers in recent years. Many of these, however, suffer from being overly complex, thereby producing spurious forecasts, particularly over long horizons and for small, noisy data sets. In this paper, we exploit statistical learning tools, namely group regularisation and cross-validation, to provide a robust framework to construct discrete-time mortality models by automatically selecting the most appropriate functions to best describe and forecast particular data sets. Most importantly, this approach produces bespoke models using a trade-off between complexity (to draw as much insight as possible from limited data sets) and parsimony (to prevent over-fitting to noise), with this trade-off designed to have specific regard to the forecasting horizon of interest. This is illustrated using both empirical data from the Human Mortality Database and simulated data, using code that has been made available within a user-friendly open-source R package StMoMo.

Keywords

Mortality projectionregularisationcross-validationage-period-cohort model
Type
Research Article
Information
ASTIN Bulletin: The Journal of the IAA , Volume 52 , Issue 1 , January 2022 , pp. 247 - 289
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The International Actuarial Association

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