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A Study on Link Functions for Modelling and Forecasting Old-Age Survival Probabilities of Australia and New Zealand

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  • Jacie Jia Liu

    (Department of Economics, Finance and Property, School of Business, Western Sydney University, Narellan Rd & Gilchrist Dr, Campbelltown, NSW 2560, Australia)

Abstract

Forecasting survival probabilities and life expectancies is an important exercise for actuaries, demographers, and social planners. In this paper, we examine extensively a number of link functions on survival probabilities and model the evolution of period survival curves of lives aged 60 over time for the elderly populations in Australasia. The link functions under examination include the newly proposed gevit and gevmin, which are compared against the traditional ones like probit, complementary log-log, and logit. We project the model parameters and so the survival probabilities into the future, from which life expectancies at old ages can be forecasted. We find that some of these models on survival probabilities, particularly those based on the new links, can provide superior fitting results and forecasting performances when compared to the more conventional approach of modelling mortality rates. Furthermore, we demonstrate how these survival probability models can be extended to incorporate extra explanatory variables such as macroeconomic factors in order to further improve the forecasting performance.

Suggested Citation

  • Jacie Jia Liu, 2021. "A Study on Link Functions for Modelling and Forecasting Old-Age Survival Probabilities of Australia and New Zealand," Risks, MDPI, vol. 9(1), pages 1-18, January.
  • Handle: RePEc:gam:jrisks:v:9:y:2021:i:1:p:11-:d:473947
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    References listed on IDEAS

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    1. Jong, Piet De & Marshall, Claymore, 2007. "Mortality Projection Based on the Wang Transform," ASTIN Bulletin, Cambridge University Press, vol. 37(1), pages 149-161, May.
    2. Wong, Chi Heem & Tsui, Albert K., 2015. "Forecasting life expectancy: Evidence from a new survival function," Insurance: Mathematics and Economics, Elsevier, vol. 65(C), pages 208-226.
    3. 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.
    4. Christopher J. Ruhm, 2000. "Are Recessions Good for Your Health?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 115(2), pages 617-650.
    5. 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.
    6. 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.
    7. Jia Liu & Jackie Li, 2019. "Beyond the highest life expectancy: construction of proxy upper and lower life expectancy bounds," Journal of Population Research, Springer, vol. 36(2), pages 159-181, June.
    8. Wong, Chi Heem & Tsui, Albert K, 2015. "Forecasting Life Expectancy: Evidence from a New Survival Function," CEI Working Paper Series 2015-1, Center for Economic Institutions, Institute of Economic Research, Hitotsubashi University.
    9. 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.
    10. Geng Niu & Bertrand Melenberg, 2014. "Trends in Mortality Decrease and Economic Growth," Demography, Springer;Population Association of America (PAA), vol. 51(5), pages 1755-1773, October.
    11. Chong It Tan & Jackie Li & Johnny Siu-Hang Li & Uditha Balasooriya, 2016. "Stochastic modelling of the hybrid survival curve," Journal of Population Research, Springer, vol. 33(4), pages 307-331, December.
    12. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    13. Hatzopoulos, P. & Haberman, S., 2015. "Modeling trends in cohort survival probabilities," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 162-179.
    14. 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.
    15. Tim J. Boonen & Hong Li, 2017. "Modeling and Forecasting Mortality With Economic Growth: A Multipopulation Approach," Demography, Springer;Population Association of America (PAA), vol. 54(5), pages 1921-1946, October.
    16. Siu Cheung & Jean-Marie Robine & Edward Tu & Graziella Caselli, 2005. "Three dimensions of the survival curve: horizontalization, verticalization, and longevity extension," Demography, Springer;Population Association of America (PAA), vol. 42(2), pages 243-258, May.
    17. Katja Hanewald, 2011. "Explaining Mortality Dynamics," North American Actuarial Journal, Taylor & Francis Journals, vol. 15(2), pages 290-314.
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