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Examining structural shifts in mortality using the Lee-Carter method

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Listed:
  • Lawrence R. Carter
  • Alexia Prskawetz

    (Max Planck Institute for Demographic Research, Rostock, Germany)

Abstract

We present an extension of the Lee-Carter method of modeling mortality to examine structural shifts in trajectories of mortality. Austrian data consisting of 53 years of single-age mortality rates are subdivided into 30 24-year submatrices. Using singular value decomposition, the submatrices are decomposed into three component submatrices: 1) the multiple realizations of the index of mortality to which each respective age-specific death rate is linearly related; 2) the average shape across age of the log of mortality schedules; 3) the sensitivity of the log of mortality at each age to variations in the elements of the index of mortality. We refer to these latter submatrices to locate structural changes in mortality patterns. A comparison of the observed and estimated life expectancy indicates that the extended Lee-Carter method is superior to the original Lee-Carter method, particularly so for life expectancies at higher ages. We conclude by projecting life expectancy up to 2050, applying the Lee-Carter method to the whole time series (1947-1999) and comparing it to an application of the Lee-Carter method to the latest subsample (1976-1999). (AUTHORS)

Suggested Citation

  • Lawrence R. Carter & Alexia Prskawetz, 2001. "Examining structural shifts in mortality using the Lee-Carter method," MPIDR Working Papers WP-2001-007, Max Planck Institute for Demographic Research, Rostock, Germany.
    • Handle: RePEc:dem:wpaper:wp-2001-007
    DOI: 10.4054/MPIDR-WP-2001-007
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    Citations

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    Cited by:

    1. Han Lin Shang & Steven Haberman, 2020. "Retiree Mortality Forecasting: A Partial Age-Range or a Full Age-Range Model?," Risks, MDPI, vol. 8(3), pages 1-11, July.
    2. Ahmadi, Seyed Saeed & Li, Johnny Siu-Hang, 2014. "Coherent mortality forecasting with generalized linear models: A modified time-transformation approach," Insurance: Mathematics and Economics, Elsevier, vol. 59(C), pages 194-221.
    3. Debón, A. & Montes, F. & Puig, F., 2008. "Modelling and forecasting mortality in Spain," European Journal of Operational Research, Elsevier, vol. 189(3), pages 624-637, September.
    4. Jorge Bravo, 2011. "Pricing Longevity Bonds Using Affine-Jump Diffusion Models," CEFAGE-UE Working Papers 2011_29, University of Evora, CEFAGE-UE (Portugal).
    5. de Jong, Piet & Tickle, Leonie & Xu, Jianhui, 2016. "Coherent modeling of male and female mortality using Lee–Carter in a complex number framework," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 130-137.
    6. John Bongaarts, 2005. "Long-range trends in adult mortality: Models and projection methods," Demography, Springer;Population Association of America (PAA), vol. 42(1), pages 23-49, February.
    7. Han Lin Shang & Rob J Hyndman & Heather Booth, 2010. "A comparison of ten principal component methods for forecasting mortality rates," Monash Econometrics and Business Statistics Working Papers 8/10, Monash University, Department of Econometrics and Business Statistics.
    8. Péter Vékás, 2020. "Rotation of the age pattern of mortality improvements in the European Union," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(3), pages 1031-1048, September.
    9. 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.
    10. O'Hare, Colin & Li, Youwei, 2014. "Identifying structural breaks in stochastic mortality models," MPRA Paper 62994, University Library of Munich, Germany.

    More about this item

    JEL classification:

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

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