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Credible Regression Approaches to Forecast Mortality for Populations with Limited Data

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  • Apostolos Bozikas

    (Department of Statistics and Insurance Science, University of Piraeus, 18534 Piraeus, Greece)

  • Georgios Pitselis

    (Department of Statistics and Insurance Science, University of Piraeus, 18534 Piraeus, Greece)

Abstract

In this paper, we propose a credible regression approach with random coefficients to model and forecast the mortality dynamics of a given population with limited data. Age-specific mortality rates are modelled and extrapolation methods are utilized to estimate future mortality rates. The results on Greek mortality data indicate that credibility regression contributed to more accurate forecasts than those produced from the Lee–Carter and Cairns–Blake–Dowd models. An application on pricing insurance-related products is also provided.

Suggested Citation

  • Apostolos Bozikas & Georgios Pitselis, 2019. "Credible Regression Approaches to Forecast Mortality for Populations with Limited Data," Risks, MDPI, vol. 7(1), pages 1-22, February.
    • Handle: RePEc:gam:jrisks:v:7:y:2019:i:1:p:27-:d:209273
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    References listed on IDEAS

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    1. Schinzinger, Edo & Denuit, Michel & Christiansen, Marcus, 2016. "A multivariate evolutionary credibility model for mortality improvement rates," LIDAM Reprints ISBA 2016019, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Plat, Richard, 2009. "On stochastic mortality modeling," Insurance: Mathematics and Economics, Elsevier, vol. 45(3), pages 393-404, December.
    3. Cary Chi-Liang Tsai & Shuai Yang, 2015. "A Linear Regression Approach to Modeling Mortality Rates of Different Forms," North American Actuarial Journal, Taylor & Francis Journals, vol. 19(1), pages 1-23, January.
    4. Salhi, Yahia & Thérond, Pierre-E., 2018. "Age-Specific Adjustment Of Graduated Mortality," ASTIN Bulletin, Cambridge University Press, vol. 48(2), pages 543-569, May.
    5. Huang, Fei & Browne, Bridget, 2017. "Mortality forecasting using a modified Continuous Mortality Investigation Mortality Projections Model for China I: methodology and country-level results," Annals of Actuarial Science, Cambridge University Press, vol. 11(1), pages 20-45, March.
    6. 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.
    7. Hong Li & Yang Lu, 2018. "A Bayesian non-parametric model for small population mortality," Post-Print hal-02419000, HAL.
    8. Ledolter, Johannes & Klugman, Stuart & Lee, Chang-Soo, 1991. "Credibility Models with Time-Varying Trend Components," ASTIN Bulletin, Cambridge University Press, vol. 21(1), pages 73-91, April.
    9. Renshaw, A.E. & Haberman, S., 2006. "A cohort-based extension to the Lee-Carter model for mortality reduction factors," Insurance: Mathematics and Economics, Elsevier, vol. 38(3), pages 556-570, June.
    10. Bojuan Zhao, 2012. "A modified Lee–Carter model for analysing short-base-period data," Population Studies, Taylor & Francis Journals, vol. 66(1), pages 39-52.
    11. Yikai (Maxwell) Gong & Zhuangdi Li & Maria Milazzo & Kristen Moore & Matthew Provencher, 2018. "Credibility Methods for Individual Life Insurance," Risks, MDPI, vol. 6(4), pages 1-16, December.
    12. De Vylder, Fl., 1978. "Parameter Estimation in Credibility Theory," ASTIN Bulletin, Cambridge University Press, vol. 10(1), pages 99-112, May.
    13. 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.
    14. 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.
    15. 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.
    16. Brouhns, Natacha & Denuit, Michel & Vermunt, Jeroen K., 2002. "A Poisson log-bilinear regression approach to the construction of projected lifetables," Insurance: Mathematics and Economics, Elsevier, vol. 31(3), pages 373-393, December.
    17. Hardy, M.R. & Panjer, H.H., 1998. "A Credibility Approach to Mortality Risk," ASTIN Bulletin, Cambridge University Press, vol. 28(2), pages 269-283, November.
    18. Hong Li & Yang Lu, 2017. "A Bayesian non-parametric model for small population mortality," Post-Print hal-02084918, HAL.
    19. Hendrik Hansen, 2013. "The forecasting performance of mortality models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(1), pages 11-31, January.
    20. Schinzinger, Edo & Denuit, Michel M. & Christiansen, Marcus C., 2016. "A multivariate evolutionary credibility model for mortality improvement rates," Insurance: Mathematics and Economics, Elsevier, vol. 69(C), pages 70-81.
    21. Apostolos Bozikas & Georgios Pitselis, 2018. "An Empirical Study on Stochastic Mortality Modelling under the Age-Period-Cohort Framework: The Case of Greece with Applications to Insurance Pricing," Risks, MDPI, vol. 6(2), pages 1-34, April.
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    1. Bozikas, Apostolos & Pitselis, Georgios, 2020. "Incorporating crossed classification credibility into the Lee–Carter model for multi-population mortality data," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 353-368.

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