A Practitioner-Oriented Evaluation of Mortality Forecasting Methods: The Case of Australia

Practitioners seeking a suitable mortality model for forecasting population by age and sex are presented with many possible choices from the large and growing academic literature on mortality forecasting. Despite this abundance, there is relatively little practical guidance on selecting the most app...

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Main Authors: Irina Grossman, Tom Wilson
Format: Article
Language:English
Published: Federal Institute for Population Research 2025-06-01
Series:Comparative Population Studies
Subjects:
Online Access:https://www.comparativepopulationstudies.de/index.php/CPoS/article/view/635
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author Irina Grossman
Tom Wilson
author_facet Irina Grossman
Tom Wilson
author_sort Irina Grossman
collection DOAJ
description Practitioners seeking a suitable mortality model for forecasting population by age and sex are presented with many possible choices from the large and growing academic literature on mortality forecasting. Despite this abundance, there is relatively little practical guidance on selecting the most appropriate models for their needs. This study evaluates the accuracy of mortality forecasting methods and provides guidance on model selection. The evaluation includes eight methods from the StMoMo and demography R packages, and a benchmark extrapolative method based on the Ediev (2008) model. We also consider the accuracy of simple combinations of individual methods. We evaluate models by preparing mortality ‘forecasts’ for Australia for past periods using data obtained from the Human Mortality Database. For each method, we created five sets of 30-year retrospective forecasts and evaluated the accuracy of the forecast mortality rates, life expectancies at birth, and life expectancy at age 65. We also evaluated the accuracy of mortality forecasts in terms of projected total deaths calculated using a pseudo-projection method. The Age-Period-Cohort model from the StMoMo R package, based on the Cairns et al. (2009) implementation, was the standout performer in our evaluation, followed by the benchmark extrapolative method. This study presents a comprehensive evaluation of mortality forecasting methods using a variety of metrics, including a new way to evaluate mortality forecasts using a pseudo-projection method. We hope that this evaluation proves useful for practitioners looking to select a mortality forecasting method.
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spelling doaj-art-717dd5b1e9284f64855fcdb09113ff192025-08-20T03:29:28ZengFederal Institute for Population ResearchComparative Population Studies1869-89801869-89992025-06-015010.12765/CPoS-2025-07542A Practitioner-Oriented Evaluation of Mortality Forecasting Methods: The Case of AustraliaIrina Grossman0https://orcid.org/0000-0002-5761-6194Tom Wilson1https://orcid.org/0000-0001-8812-7556The University of MelbourneIndependent ResearcherPractitioners seeking a suitable mortality model for forecasting population by age and sex are presented with many possible choices from the large and growing academic literature on mortality forecasting. Despite this abundance, there is relatively little practical guidance on selecting the most appropriate models for their needs. This study evaluates the accuracy of mortality forecasting methods and provides guidance on model selection. The evaluation includes eight methods from the StMoMo and demography R packages, and a benchmark extrapolative method based on the Ediev (2008) model. We also consider the accuracy of simple combinations of individual methods. We evaluate models by preparing mortality ‘forecasts’ for Australia for past periods using data obtained from the Human Mortality Database. For each method, we created five sets of 30-year retrospective forecasts and evaluated the accuracy of the forecast mortality rates, life expectancies at birth, and life expectancy at age 65. We also evaluated the accuracy of mortality forecasts in terms of projected total deaths calculated using a pseudo-projection method. The Age-Period-Cohort model from the StMoMo R package, based on the Cairns et al. (2009) implementation, was the standout performer in our evaluation, followed by the benchmark extrapolative method. This study presents a comprehensive evaluation of mortality forecasting methods using a variety of metrics, including a new way to evaluate mortality forecasts using a pseudo-projection method. We hope that this evaluation proves useful for practitioners looking to select a mortality forecasting method.https://www.comparativepopulationstudies.de/index.php/CPoS/article/view/635forecastingmortality forecasting methodslife expectancyaustraliapseudo-projection method
spellingShingle Irina Grossman
Tom Wilson
A Practitioner-Oriented Evaluation of Mortality Forecasting Methods: The Case of Australia
Comparative Population Studies
forecasting
mortality forecasting methods
life expectancy
australia
pseudo-projection method
title A Practitioner-Oriented Evaluation of Mortality Forecasting Methods: The Case of Australia
title_full A Practitioner-Oriented Evaluation of Mortality Forecasting Methods: The Case of Australia
title_fullStr A Practitioner-Oriented Evaluation of Mortality Forecasting Methods: The Case of Australia
title_full_unstemmed A Practitioner-Oriented Evaluation of Mortality Forecasting Methods: The Case of Australia
title_short A Practitioner-Oriented Evaluation of Mortality Forecasting Methods: The Case of Australia
title_sort practitioner oriented evaluation of mortality forecasting methods the case of australia
topic forecasting
mortality forecasting methods
life expectancy
australia
pseudo-projection method
url https://www.comparativepopulationstudies.de/index.php/CPoS/article/view/635
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AT tomwilson apractitionerorientedevaluationofmortalityforecastingmethodsthecaseofaustralia
AT irinagrossman practitionerorientedevaluationofmortalityforecastingmethodsthecaseofaustralia
AT tomwilson practitionerorientedevaluationofmortalityforecastingmethodsthecaseofaustralia