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An application of MCMC simulation in mortality projection for populations with limited data

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  • Jackie Li

    (Monash University)

Abstract

Objective: IIn this paper, we investigate the use of Bayesian modeling and Markov chain Monte Carlo (MCMC) simulation, via the software WinBUGS, to project future mortality for populations with limited data. In particular, we adapt some extensions of the Lee-Carter method under the Bayesian framework to allow for situations in which mortality data are scarce. Our approach would be useful for certain developing nations that have not been regularly collecting death counts and population statistics. Inferences of the model estimates and forecasts can readily be drawn from the simulated samples. Information on another population resembling the population under study can be exploited and incorporated into the prior distributions in order to facilitate the construction of probability intervals. The two sets of data can also be modeled in a joint manner. We demonstrate an application of this approach to some data from China and Taiwan.

Suggested Citation

  • Jackie Li, 2014. "An application of MCMC simulation in mortality projection for populations with limited data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 30(1), pages 1-48.
    • Handle: RePEc:dem:demres:v:30:y:2014:i:1
    DOI: 10.4054/DemRes.2014.30.1
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    References listed on IDEAS

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    1. Kogure Atsuyuki & Kitsukawa Kenji & Kurachi Yoshiyuki, 2009. "A Bayesian Comparison of Models for Changing Mortalities toward Evaluating Longevity Risk in Japan," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 3(2), pages 1-22, April.
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    4. Cairns, Andrew J.G. & Blake, David & Dowd, Kevin & Coughlan, Guy D. & Khalaf-Allah, Marwa, 2011. "Bayesian Stochastic Mortality Modelling for Two Populations," ASTIN Bulletin, Cambridge University Press, vol. 41(1), pages 29-59, May.
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    6. Ronald Lee, 2000. "The Lee-Carter Method for Forecasting Mortality, with Various Extensions and Applications," North American Actuarial Journal, Taylor & Francis Journals, vol. 4(1), pages 80-91.
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    Cited by:

    1. Qian Lu & Katja Hanewald & Xiaojun Wang, 2021. "Subnational Mortality Modelling: A Bayesian Hierarchical Model with Common Factors," Risks, MDPI, vol. 9(11), pages 1-21, November.
    2. Wong, Jackie S.T. & Forster, Jonathan J. & Smith, Peter W.F., 2018. "Bayesian mortality forecasting with overdispersion," Insurance: Mathematics and Economics, Elsevier, vol. 83(C), pages 206-221.
    3. 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.
    4. Zhenmin Cheng & Wanwan Si & Zhiwei Xu & Kaibiao Xiang, 2022. "Prediction of China’s Population Mortality under Limited Data," IJERPH, MDPI, vol. 19(19), pages 1-13, September.
    5. Jackie Li & Leonie Tickle & Nick Parr, 2016. "A multi-population evaluation of the Poisson common factor model for projecting mortality jointly for both sexes," Journal of Population Research, Springer, vol. 33(4), pages 333-360, December.

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    More about this item

    Keywords

    modelling; Bayesian approach; mortality projection; WinBUGS; Markov chain Monte Carlo (MCMC);
    All these keywords.

    JEL classification:

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

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