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Longevity forecasting by socio-economic groups using compositional data analysis

Author

Listed:
  • Søren Kjærgaard

    (University of Southern Denmark)

  • Yunus Emre Ergemen

    (University of Aarhus and CREATES)

  • Marie-Pier Bergeron Boucher

    (University of Southern Denmark)

  • Jim Oeppen

    (University of Southern Denmark)

  • Malene Kallestrup-Lamb

    (University of Aarhus and CREATES)

Abstract

Several OECD countries have recently implemented an automatic link between the statutory retirement age and life expectancy for the total population to insure sustainability in their pension systems when life expectancy is increasing. Significant mortality differentials are observed across socio-economic groups and future changes in these differentials will determine whether some socio-economic groups drive increases in the retirement age leaving other groups with fewer years in receipt of pensions. We forecast life expectancy by socio-economic groups and compare the forecast performance of competing models using Danish mortality data and find that the most accurate model assumes a common mortality trend. Life expectancy forecasts are used to analyse the consequences of a pension system where the statutory retirement age is increased when total life expectancy is increasing

Suggested Citation

  • Søren Kjærgaard & Yunus Emre Ergemen & Marie-Pier Bergeron Boucher & Jim Oeppen & Malene Kallestrup-Lamb, 2019. "Longevity forecasting by socio-economic groups using compositional data analysis," CREATES Research Papers 2019-08, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2019-08
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    References listed on IDEAS

    as
    1. Nan Li & Ronald Lee, 2005. "Coherent mortality forecasts for a group of populations: An extension of the lee-carter method," Demography, Springer;Population Association of America (PAA), vol. 42(3), pages 575-594, August.
    2. Tibor F. Liska, 2007. "The Liska model," Society and Economy, Akadémiai Kiadó, Hungary, vol. 29(3), pages 363-381, December.
    3. Cairns, Andrew J.G. & Blake, David & Dowd, Kevin & Coughlan, Guy D. & Epstein, David & Khalaf-Allah, Marwa, 2011. "Mortality density forecasts: An analysis of six stochastic mortality models," Insurance: Mathematics and Economics, Elsevier, vol. 48(3), pages 355-367, May.
    4. William N. Evans & Timothy J. Moore, 2012. "Liquidity, Economic Activity, and Mortality," The Review of Economics and Statistics, MIT Press, vol. 94(2), pages 400-418, May.
    5. Marie-Pier Bergeron-Boucher & Vladimir Canudas-Romo & James E. Oeppen & James W. Vaupel, 2017. "Coherent forecasts of mortality with compositional data analysis," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 37(17), pages 527-566.
    6. Søren Kjærgaard & Yunus Emre Ergemen & Malene Kallestrup‐Lamb & Jim Oeppen & Rune Lindahl‐Jacobsen, 2019. "Forecasting causes of death by using compositional data analysis: the case of cancer deaths," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(5), pages 1351-1370, November.
    7. 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.
    8. Clark, Todd E. & West, Kenneth D., 2006. "Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 155-186.
    9. Johansen, Soren, 1991. "Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models," Econometrica, Econometric Society, vol. 59(6), pages 1551-1580, November.
    10. Ledyard Tucker, 1966. "Some mathematical notes on three-mode factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 31(3), pages 279-311, September.
    11. Booth, H. & Tickle, L., 2008. "Mortality Modelling and Forecasting: a Review of Methods," Annals of Actuarial Science, Cambridge University Press, vol. 3(1-2), pages 3-43, September.
    12. Andrés Villegas & Steven Haberman, 2014. "On the Modeling and Forecasting of Socioeconomic Mortality Differentials: An Application to Deprivation and Mortality in England," North American Actuarial Journal, Taylor & Francis Journals, vol. 18(1), pages 168-193.
    13. Søren Kjærgaard & Yunus Emre Ergemen & Malene Kallestrup-Lamb & Jim Oeppen & Rune Lindahl-Jacobsen, 2019. "Forecasting Causes of Death using Compositional Data Analysis: the Case of Cancer Deaths," CREATES Research Papers 2019-07, Department of Economics and Business Economics, Aarhus University.
    14. Haberman, Steven & Renshaw, Arthur, 2009. "On age-period-cohort parametric mortality rate projections," Insurance: Mathematics and Economics, Elsevier, vol. 45(2), pages 255-270, October.
    15. Ronald Lee & Timothy Miller, 2001. "Evaluating the performance of the lee-carter method for forecasting mortality," Demography, Springer;Population Association of America (PAA), vol. 38(4), pages 537-549, November.
    16. Andrew J. G. Cairns & David Blake & Kevin Dowd, 2006. "A Two‐Factor Model for Stochastic Mortality with Parameter Uncertainty: Theory and Calibration," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 73(4), pages 687-718, December.
    17. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
    18. Andrew Cairns & David Blake & Kevin Dowd & Guy Coughlan & David Epstein & Alen Ong & Igor Balevich, 2009. "A Quantitative Comparison of Stochastic Mortality Models Using Data From England and Wales and the United States," North American Actuarial Journal, Taylor & Francis Journals, vol. 13(1), pages 1-35.
    19. Declan French & Colin O'Hare, 2014. "Forecasting Death Rates Using Exogenous Determinants," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(8), pages 640-650, December.
    20. Hallin, Marc & Liska, Roman, 2007. "Determining the Number of Factors in the General Dynamic Factor Model," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 603-617, June.
    21. Marie-Pier Bergeron-Boucher & Violetta Simonacci & Jim Oeppen & Michele Gallo, 2018. "Coherent Modeling and Forecasting of Mortality Patterns for Subpopulations Using Multiway Analysis of Compositions: An Application to Canadian Provinces and Territories," North American Actuarial Journal, Taylor & Francis Journals, vol. 22(1), pages 92-118, January.
    22. Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
    23. Stephen E. Snyder & William N. Evans, 2006. "The Effect of Income on Mortality: Evidence from the Social Security Notch," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 482-495, August.
    24. France Meslé & Jacques Vallin, 2006. "Diverging Trends in Female Old‐Age Mortality: The United States and the Netherlands versus France and Japan," Population and Development Review, The Population Council, Inc., vol. 32(1), pages 123-145, March.
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    More about this item

    Keywords

    Compositional data; forecasting; longevity; pension; socioeconomic groups;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior

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