Advertisement

Modeling stochastic mortality with O-U type processes

  • Jing Zheng
  • Chang-qing Tong
  • Gui-jun Zhang
Article
  • 34 Downloads

Abstract

Modeling log-mortality rates on O-U type processes and forecasting life expectancies are explored using U.S. data. In the classic Lee-Carter model of mortality, the time trend and the age-specific pattern of mortality over age group are linear, this is not the feature of mortality model. To avoid this disadvantage, O-U type processes will be used to model the log-mortality in this paper. In fact, this model is an AR(1) process, but with a nonlinear time drift term. Based on the mortality data of America from Human Mortality database (HMD), mortality projection consistently indicates a preference for mortality with O-U type processes over those with the classical Lee-Carter model. By means of this model, the low bounds of mortality rates at every age are given. Therefore, lengthening of maximum life expectancies span is estimated in this paper.

Keywords

mortality stochastic forecasting O-U type process 

MR Subject Classification

62F12 62M05 60H10 60J60 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    J Bongaarts. Long-range trends in adult mortality: models and projection, Demography, 2005, 42(1): 23–49.CrossRefGoogle Scholar
  2. [2]
    H Booth, J Maindonald, L Smith. Applying Lee-Carter under conditions of variable mortality decline, Popul Stud, 2002, 56: 325–336.CrossRefGoogle Scholar
  3. [3]
    H Booth H, J Maindonald, L Smith. Age-time interactions in mortality projection: Applying Lee-Carter to Australia, Working papers in demography, Australian National University, 2002.Google Scholar
  4. [4]
    H Booth, L Tickle. Mortality modeling and forecasting: A review of methods, Ann Actuar Sci, 3(1/2): 3–43.Google Scholar
  5. [5]
    N Brouhns, M Denuit, J K Vermunt. A Poisson log-bilinear regression approach to the construction of projected life tables, Insurance Math Econom, 2002, 31(3): 373–393.MathSciNetzbMATHCrossRefGoogle Scholar
  6. [6]
    Z Butt, S Haberman. A comparative study of parametric mortality projection models, Insurance Math Econom, 2011, 48(1): 35–55MathSciNetCrossRefGoogle Scholar
  7. [7]
    A J Cairns, D Blake, K Dowd. A two-factor model for stochastic mortality with parameter uncertainty: theory and calibration, J Risk Insur, 2006, 73(4): 687–718.CrossRefGoogle Scholar
  8. [8]
    F Denton, C Feaver, B Spencer. Time series analysis and stochastic forecasting: an econometric study of mortality and life expectancy, J Popul Econ, 2005, 18(2): 203–227.Google Scholar
  9. [9]
    R J Hyndman, M S Ullah. Robust forecasting of mortality and fertility rate: A functional data approach, Comput Statist Data Anal, 2007, 51: 4942–4956.MathSciNetzbMATHCrossRefGoogle Scholar
  10. [10]
    R D Lee, L R Carter. Modeling and forecasting US mortality, J Amer Statist Assoc, 1992, 87(419): 659–671.zbMATHGoogle Scholar
  11. [11]
    R Plat. On stochastic mortality modelling, Insurance Math Econom, 2009, 45: 393–404.MathSciNetzbMATHCrossRefGoogle Scholar
  12. [12]
    A Rogers, F Planck. Model: a general program for estimating parameterized model schedules of fertility, mortality, migration, and marital and labor force status transitions, International Institute for Applied Systems Analysis, Laxenburg, Austria, 1983.Google Scholar
  13. [13]
    K-I Sato. Lévy Processes and Infinitely Divisible Distributions, Cambridge University Press, Cambridge, 1999.zbMATHGoogle Scholar

Copyright information

© Editorial Committee of Applied Mathematics-A Journal of Chinese Universities and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of EconomicsHangzhou Dianzi UniversityHangzhouChina

Personalised recommendations