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Prediction of China’s Population Mortality under Limited Data

Author

Listed:
  • Zhenmin Cheng

    (School of Mathematics and Statistics, Guizhou University, Guiyang 550025, China)

  • Wanwan Si

    (School of Mathematics and Statistics, Guizhou University, Guiyang 550025, China)

  • Zhiwei Xu

    (School of Mathematics and Statistics, Guizhou University, Guiyang 550025, China)

  • Kaibiao Xiang

    (School of Management, Guizhou University, Guiyang 550025, China)

Abstract

Population mortality is an important step in quantifying the risk of longevity. China lacks data on population mortality, especially the elderly population. Therefore, this paper first uses spline fitting to supplement the missing data and then uses dynamic models to predict the species mortality of the Chinese population, including age extrapolation and trend extrapolation. Firstly, for age extrapolation, kannisto is used to expand the data of the high-age population. Secondly, the Lee-Carter single-factor model is used to predict gender and age mortality. This paper fills and smoothes the deficiencies of the original data to make up for the deficiencies of our population mortality data and improve the prediction accuracy of population mortality and life expectancy, while analyzing the impact of mortality improvement and providing a theoretical basis for policies to deal with the risk of longevity.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:19:p:12371-:d:928287
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    References listed on IDEAS

    as
    1. 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.
    2. Susanna Levantesi & Virginia Pizzorusso, 2019. "Application of Machine Learning to Mortality Modeling and Forecasting," Risks, MDPI, vol. 7(1), pages 1-19, February.
    3. Zhiwei Liu & Yonglei Fang & Lei Ma, 2022. "A Study on the Impact of Population Age Structure Change on Economic Growth in China," Sustainability, MDPI, vol. 14(7), pages 1-15, March.
    4. Jaap Spreeuw & Iqbal Owadally & Muhammad Kashif, 2022. "Projecting Mortality Rates Using a Markov Chain," Mathematics, MDPI, vol. 10(7), pages 1-18, April.
    5. Anastasia Novokreshchenova, 2016. "Predicting Human Mortality: Quantitative Evaluation of Four Stochastic Models," Risks, MDPI, vol. 4(4), pages 1-28, December.
    6. Renshaw, A. E. & Haberman, S., 2003. "Lee-Carter mortality forecasting with age-specific enhancement," Insurance: Mathematics and Economics, Elsevier, vol. 33(2), pages 255-272, October.
    7. 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.
    8. He, Lingyu & Huang, Fei & Shi, Jianjie & Yang, Yanrong, 2021. "Mortality forecasting using factor models: Time-varying or time-invariant factor loadings?," Insurance: Mathematics and Economics, Elsevier, vol. 98(C), pages 14-34.
    9. 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.
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