Browse > Article
http://dx.doi.org/10.5351/KJAS.2021.34.3.347

A comparative study of stochastic mortality models considering cohort effects  

Kim, Soon-Young (Statistics Research Institute, Statistics Korea)
Publication Information
The Korean Journal of Applied Statistics / v.34, no.3, 2021 , pp. 347-373 More about this Journal
Abstract
Over the past 50 years, explorative research on the nation's mortality decline patterns has showed a decrease in age-specific mortality rates in all age groups, but there were different improvement patterns in specific mortality rates depending on ages and periods. Greater distinct improvement was observed in mortality rates among women than men, and there was a noticeable improvement in mortality rates in certain groups especially in the more recent decades, revealing a structural change in the overall trends regarding death periods. In this paper, we compare various stochastic mortality models considering cohort effects for mortality projection using Korean female mortality data and further explore the uncertainty related to projection. It also created age-specific mortality rates and life expectancy for women until 2067 based on the results of the analysis, and compared them with future age-specific mortality rates and life expectancy provided by the national statistical office (KOISIS). The best optimal model could vary depending on data usage periods. however, considering the overall fit and predictability, the PLAT model would be regarded to have appropriate predictability in terms of the mortality rates of women in South Korea.
Keywords
cohort effects; mortality projection model; age-specific mortality rates; life expectancy;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Li N, Lee R, and Gerland P (2013). Extending the Lee-Cater method to model the rotation of age patterns of mortality decline for long-term projections, Demography, 50, 2037-2051.   DOI
2 O'Hare C and Li Y (2012). Explaining young mortality, Insurance: Mathematics and Economics, 50, 12-25.   DOI
3 Plat R (2009). On stochastic mortality modeling, Insurance: Mathematics and Economics, 45, 393-404.   DOI
4 Villegas AM, Millossovich P, and Kaishev VK (2017). StMoMo: An R Package for Stochastic Mortality Modelling, R package version 0.4.0, Retrieved from: http://CRAN.R-project.org/package=StMoMo
5 Alai DH and Sherris M (2014). Rethinking age-period-cohort mortality trend models, Scandinavian Actuarial Journal, 3, 208-227.   DOI
6 Booth H, Hyndman RJ, Tickle L, and De Jong P (2006). Lee-carter mortality forecasting: a multi-country comparison of variants and extensions, Demographic Research, 15, 289-310.   DOI
7 Cairns AJG, Blake D, and Dowd K (2006). A two-factor model for stochastic mortality with parameter uncertainty: Theory and calibration, Journal of Risk and Insurance, 73, 687-718.   DOI
8 Aro H and Pennanen T (2011). A user-friendly approach to stochastic mortality modelling, European Actuarial Journal, 1, 151-167.   DOI
9 De Jong P and Tickle L (2006). Extending lee-carter mortality forecasting, Mathematical Population Studies, 13, 1-18.   DOI
10 Kim SY (2020). A study on the decomposition of contributions by age to changes in life expectancy at birth in Korea and visualization of mortality improvement, Journal of The Korean Official Statistics, 25, 1-31.
11 B'orger M, Fleischer D, and Kuksin N (2014). Modeling the mortality trend under modern solvency regimes, Astin Bulletin, 44, 1-38.   DOI
12 Brouhns N, Denuit M, and Vermunt J (2002). A poisson log-bilinear regression approach to the construction of projected life tables, Insurance: Mathematics and Economics, 31, 373-393.   DOI
13 Hunt A and Villegas AM (2015). Robustness and Convergence in the Lee-Carter model with cohorts, Insurance: Mathematics and Economics, 64, 186-202.   DOI
14 Butt Z, Haberman S, and Shang HL (2014). Ilc: lee-carter mortality models using iterative fitting algorithms, R package version 1.0, Retrieved from: http://CRAN.R-project.org/package=ilc
15 Cairns AJG, Blake D, Dowd K, Coughlan GD, Epstein D, Ong A, and Balevich I (2009). A quantitative comparison of stochastic mortality models using data from England and Wales and the United States, North American Actuarial Journal, 13, 1-35.   DOI
16 Cairns AJG, Blake D, Dowd K, Coughlan GD, Epstein D, and Khalaf-Allah M (2011). Mortality density forecasts: an analysis of six stochastic mortality models, Insurance: Mathematics and Economics, 48, 355-367.   DOI
17 Jung KN, Baek JS, and Kim DG (2013). Comparison of mortality estimate and prediction by the period of time series data used, The Korean Journal of Applied Statistics, 26, 1019-1032.   DOI
18 Kim SJ (2012b), A study on the prediction of mortality rate using the lee-carter model, The Journal of actuarial science, 4, 47-66
19 Booth H, Tickle L, and Smith L (2005). Evaluation of the variants of the lee-carter method of forecasting mortality: a multi-country comparison, New Zealand Population Review, 31, 13-34.
20 Oh JH and Kim SY (2018). Consideration on assumption and transition of mortality model for Korea, The Korean Journal of Applied Statistics, 31, 637-653.   DOI
21 Renshaw AE and Haberman S (2006). A cohort-based extension to the lee-carter model for mortality reduction factors, Insurance: Mathematics and Economics, 38, 556-570.   DOI
22 Villegas AM, Haberman S, Kaishev VK, and Millossovich P (2015). A Comparative Study of Two-Population Models for the Assessment of Basis Risk in Longevity Hedges, Working paper.
23 Kim SY, Oh JH, and Kim KW (2018). A comparison mortality projection by different time period in time series, The Korean Journal of Applied Statistics, 31, 41-65.   DOI
24 Jeong S and Kim KW (2011). A comparison study for mortality forecasting models by average life expectancy, The Korean Journal of Applied Statistics, 24, 115-125.   DOI
25 Park YS, Kim KW, Lee DH, and Lee YK (2005). A comparison of two models for forecasting mortality in South Korea, The Korean Journal of Applied Statistics, 18, 639-654.   DOI
26 Kim S Y and Oh JH (2017). A study comparison of mortality projection using parametric and non-parametric model, The Korean Journal of Applied Statistics, 30, 701-717.   DOI
27 Haberman S and Renshaw A (2011). A comparative study of parametric mortality projection models, Insurance: Mathematics and Economics, 48, 35-55.   DOI
28 Hunt A and Blake D (2015). Modelling longevity bonds: analysing the Swiss Re Kortis bond, Insurance: Mathematics and Economics, Retrieved from http://dx.doi.org/10.1016/j.insmatheco.2015.03.017   DOI
29 Hyndman RJ and Khandakar Y (2008). Automatic time series forecasting: the forecast package for R, Journal of Statistical Software, 27, Retrieved from 00 00, http://www.jstatsoft.org/v27/i03/, 1-22.
30 Kang JC, Lee DS, and Shung JH (2006). A study on the method for forecasting mortality considering longevity risk, The Journal of Risk Management, 17, 153-178.
31 Kim SJ (2012a), A comparison study on the stochastic mortality models for measuring longevity risk, Korean Insurance Journal, 93, 213-235.
32 Lee RD and Carter LR (1992). Modeling and forecasting US mortality, Journal of the American Statistical Association, 87, 659-671.   DOI
33 Lee R and Miller T (2001). Evaluating the performance of the lee-carter method for forecasting mortality, Demography, 38, 537-549   DOI
34 Lov'asz E (2011). Analysis of Finnish and Swedish mortality data with stochastic mortality models, European Actuarial Journal, 1, 259-289.   DOI
35 McCullagh P and Nelder J (1989). Generalized linear models(2nd ed), Chapman & Hall, London.
36 Currie ID (2016). On fitting generalized linear and non-linear models of mortality, Scandinavian Actuarial Journal, 4, 356-383.   DOI