IDEAS home Printed from https://ideas.repec.org/p/zbw/sfb649/sfb649dp2016-023.html
   My bibliography  Save this paper

A mortality model for multi-populations: A semi-parametric approach

Author

Listed:
  • Fang, Lei
  • Härdle, Wolfgang Karl
  • Park, Juhyun

Abstract

Mortality is different across countries, states and regions. Several empirical research works however reveal that mortality trends exhibit a common pattern and show similar structures across populations. The key element in analyzing mortality rate is a time-varying indicator curve. Our main interest lies in validating the existence of the common trends among these curves, the similar gender differences and their variability in location among the curves at the national level. Motivated by the empirical findings, we make the study of estimating and forecasting mortality rates based on a semi-parametric approach, which is applied to multiple curves with the shape-related nonlinear variation. This approach allows us to capture the common features contained in the curve functions and meanwhile provides the possibility to characterize the nonlinear variation via a few deviation parameters. These parameters carry an instructive summary of the time-varying curve functions and can be further used to make a suggestive forecast analysis for countries with barren data sets. In this research the model is illustrated with mortality rates of Japan and China, and extended to incorporate more countries. All numerical procedures are transparent and reproduced on www.quantlet.de.

Suggested Citation

  • Fang, Lei & Härdle, Wolfgang Karl & Park, Juhyun, 2016. "A mortality model for multi-populations: A semi-parametric approach," SFB 649 Discussion Papers 2016-023, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  • Handle: RePEc:zbw:sfb649:sfb649dp2016-023
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/146192/1/861805380.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fang, Lei & Härdle, Wolfgang Karl, 2015. "Stochastic population analysis: A functional data approach," SFB 649 Discussion Papers 2015-007, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    2. Booth, Heather, 2006. "Demographic forecasting: 1980 to 2005 in review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 547-581.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. repec:hum:wpaper:sfb649dp2016-023 is not listed on IDEAS
    2. Valeria D’Amato & Emilia Di Lorenzo & Steven Haberman & Maria Russolillo & Marilena Sibillo, 2011. "The Poisson Log-Bilinear Lee-Carter Model," North American Actuarial Journal, Taylor & Francis Journals, vol. 15(2), pages 315-333.
    3. repec:hum:wpaper:sfb649dp2009-015 is not listed on IDEAS
    4. Alonso Meseguer, Javier & Tuesta Cárdenas, David & Torres Torres, Diego & Villamide Muiña, Begoña, 2015. "Proyecciones de tablas generacionales dinámicas de mortalidad y riesgo de longevidad en países en vías de desarrollo: El caso chileno/Projections of Dynamic Generational Mortality Tables and Longevity," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 33, pages 941-964, Septiembr.
    5. Beutner, Eric & Reese, Simon & Urbain, Jean-Pierre, 2017. "Identifiability issues of age–period and age–period–cohort models of the Lee–Carter type," Insurance: Mathematics and Economics, Elsevier, vol. 75(C), pages 117-125.
    6. Katja Hanewald & Thomas Post & Helmut Gründl, 2011. "Stochastic Mortality, Macroeconomic Risks and Life Insurer Solvency," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 36(3), pages 458-475, July.
    7. 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.
    8. Arkadiusz Wiśniowski & Peter Smith & Jakub Bijak & James Raymer & Jonathan Forster, 2015. "Bayesian Population Forecasting: Extending the Lee-Carter Method," Demography, Springer;Population Association of America (PAA), vol. 52(3), pages 1035-1059, June.
    9. S⊘ren Kjærgaard & Yunus Emre Ergemen & Marie‐Pier Bergeron‐Boucher & Jim Oeppen & Malene Kallestrup‐Lamb, 2020. "Longevity forecasting by socio‐economic groups using compositional data analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1167-1187, June.
    10. Tomas, Julien & Planchet, Frédéric, 2015. "Prospective mortality tables: Taking heterogeneity into account," Insurance: Mathematics and Economics, Elsevier, vol. 63(C), pages 169-190.
    11. repec:hum:wpaper:sfb649dp2009-008 is not listed on IDEAS
    12. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    13. Carlo G. Camarda & Ugofilippo Basellini, 2021. "Smoothing, Decomposing and Forecasting Mortality Rates," European Journal of Population, Springer;European Association for Population Studies, vol. 37(3), pages 569-602, July.
    14. 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.
    15. Hanewald, Katja, 2009. "Lee-Carter and the macroeconomy," SFB 649 Discussion Papers 2009-008, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    16. Shang, Han Lin & Haberman, Steven, 2017. "Grouped multivariate and functional time series forecasting:An application to annuity pricing," Insurance: Mathematics and Economics, Elsevier, vol. 75(C), pages 166-179.
    17. Han Lin Shang & Rob J Hyndman & Heather Booth, 2010. "A comparison of ten principal component methods for forecasting mortality rates," Monash Econometrics and Business Statistics Working Papers 8/10, Monash University, Department of Econometrics and Business Statistics.
    18. Dunstan Kim & Ball Christopher, 2016. "Demographic Projections: User and Producer Experiences of Adopting a Stochastic Approach," Journal of Official Statistics, Sciendo, vol. 32(4), pages 947-962, December.
    19. Francesco Billari & Rebecca Graziani & Eugenio Melilli, 2014. "Stochastic Population Forecasting Based on Combinations of Expert Evaluations Within the Bayesian Paradigm," Demography, Springer;Population Association of America (PAA), vol. 51(5), pages 1933-1954, October.
    20. Shang, Han Lin & Haberman, Steven & Xu, Ruofan, 2022. "Multi-population modelling and forecasting life-table death counts," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 239-253.
    21. Wang, Hong & Koo, Bonsoo & O'Hare, Colin, 2016. "Retirement planning in the light of changing demographics," Economic Modelling, Elsevier, vol. 52(PB), pages 749-763.
    22. Lanza Queiroz, Bernardo & Lobo Alves Ferreira, Matheus, 2021. "The evolution of labor force participation and the expected length of retirement in Brazil," The Journal of the Economics of Ageing, Elsevier, vol. 18(C).
    23. 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.

    More about this item

    Keywords

    Nonparametric smoothing; Parametric modeling; Common trend; Mortality; Lee-Carter method; Multi-populations;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:sfb649:sfb649dp2016-023. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/sohubde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.