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Point and interval forecasts of mortality rates and life expectancy: A comparison of ten principal component methods

Author

Listed:
  • Han Lin Shang

    (Australian National University)

  • Heather Booth

    (Australian National University)

  • Rob Hyndman

    (Monash University)

Abstract

Using the age- and sex-specific data of 14 developed countries, we compare the point and interval forecast accuracy and bias of ten principal component methods for forecasting mortality rates and life expectancy. The ten methods are variants and extensions of the Lee-Carter method. Based on one-step forecast errors, the weighted Hyndman-Ullah method provides the most accurate point forecasts of mortality rates and the Lee-Miller method is the least biased. For the accuracy and bias of life expectancy, the weighted Hyndman-Ullah method performs the best for female mortality and the Lee-Miller method for male mortality. While all methods underestimate variability in mortality rates, the more complex Hyndman-Ullah methods are more accurate than the simpler methods. The weighted Hyndman-Ullah method provides the most accurate interval forecasts for mortality rates, while the robust Hyndman-Ullah method provides the best interval forecast accuracy for life expectancy.

Suggested Citation

  • Han Lin Shang & Heather Booth & Rob Hyndman, 2011. "Point and interval forecasts of mortality rates and life expectancy: A comparison of ten principal component methods," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 25(5), pages 173-214, July.
  • Handle: RePEc:dem:demres:v:25:y:2011:i:5
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Rob Hyndman & Heather Booth & Farah Yasmeen, 2013. "Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models," Demography, Springer;Population Association of America (PAA), vol. 50(1), pages 261-283, February.
    2. repec:eee:insuma:v:75:y:2017:i:c:p:166-179 is not listed on IDEAS
    3. Adrian E. Raftery & Nevena Lalic & Patrick Gerland, 2014. "Joint probabilistic projection of female and male life expectancy," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 30(27), pages 795-822, March.
    4. Lenny Stoeldraijer & Coen van Duin & Leo van Wissen & Fanny Janssen, 2013. "Impact of different mortality forecasting methods and explicit assumptions on projected future life expectancy: The case of the Netherlands," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 29(13), pages 323-354, August.
    5. Han Lin Shang, 2012. "Point and interval forecasts of age-specific life expectancies," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 27(21), pages 593-644, November.
    6. repec:gam:jrisks:v:6:y:2018:i:2:p:44-:d:142754 is not listed on IDEAS
    7. Syazreen Shair & Sachi Purcal & Nick Parr, 2017. "Evaluating Extensions to Coherent Mortality Forecasting Models," Risks, MDPI, Open Access Journal, vol. 5(1), pages 1-20, March.
    8. Man Chung Fung & Gareth W. Peters & Pavel V. Shevchenko, 2017. "Cohort effects in mortality modelling: a Bayesian state-space approach," Papers 1703.08282, arXiv.org.
    9. F. Peters & J. P. Mackenbach & W. J. Nusselder, 2016. "Does the Impact of the Tobacco Epidemic Explain Structural Changes in the Decline of Mortality?," European Journal of Population, Springer;European Association for Population Studies, vol. 32(5), pages 687-702, December.
    10. Fanny Janssen & Leo Wissen & Anton Kunst, 2013. "Including the Smoking Epidemic in Internationally Coherent Mortality Projections," Demography, Springer;Population Association of America (PAA), vol. 50(4), pages 1341-1362, August.
    11. Francesco Billari & Rebecca Graziani & Eugenio Melilli, 2014. "Stochastic Population Forecasting Based on Combinations of Expert Evaluations Within the Bayesian Paradigm," Demography, Springer;Population Association of America (PAA), vol. 51(5), pages 1933-1954, October.
    12. Lei Fang & Wolfgang K. Härdle, 2015. "Stochastic Population Analysis: A Functional Data Approach," SFB 649 Discussion Papers SFB649DP2015-007, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    13. Han Lin Shang, 2012. "Point and interval forecasts of age-specific fertility rates: a comparison of functional principal component methods," Monash Econometrics and Business Statistics Working Papers 10/12, Monash University, Department of Econometrics and Business Statistics.
    14. Man Chung Fung & Gareth W. Peters & Pavel V. Shevchenko, 2016. "A unified approach to mortality modelling using state-space framework: characterisation, identification, estimation and forecasting," Papers 1605.09484, arXiv.org.
    15. repec:spr:demogr:v:54:y:2017:i:4:d:10.1007_s13524-017-0584-0 is not listed on IDEAS
    16. Alexander Dokumentov & Rob J Hyndman, 2014. "Low-dimensional decomposition, smoothing and forecasting of sparse functional data," Monash Econometrics and Business Statistics Working Papers 16/14, Monash University, Department of Econometrics and Business Statistics.
    17. Tickle Leonie & Booth Heather, 2014. "The Longevity Prospects of Australian Seniors: An Evaluation of Forecast Method and Outcome," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 8(2), pages 1-34, July.
    18. Dorina Lazar & Anuta Buiga & Adela Deaconu, 2016. "Common Stochastic Trends in European Mortality Levels: Testing and Consequences for Modeling Longevity Risk in Insurance," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 152-168, June.
    19. Sergei Scherbov & Dalkhat Ediev, 2016. "Does selection of mortality model make a difference in projecting population ageing?," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 34(2), pages 39-62, January.
    20. Nan Li & Ronald Lee & Patrick Gerland, 2013. "Extending the Lee-Carter Method to Model the Rotation of Age Patterns of Mortality Decline for Long-Term Projections," Demography, Springer;Population Association of America (PAA), vol. 50(6), pages 2037-2051, December.

    More about this item

    Keywords

    forecasting; forecasting time series; interval forecasts; Lee-Carter model; life expectancy; mortality forecasting; principal components analysis;

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

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