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http://dx.doi.org/10.5351/KJAS.2018.31.5.637

Consideration on assumption and transition of mortality model for Korea - Discussion on the kinds of Lee-carter -  

Oh, Jinho (Statistical Research Institute, Statistics Korea)
Kim, Soon-Young (Statistical Research Institute, Statistics Korea)
Publication Information
The Korean Journal of Applied Statistics / v.31, no.5, 2018 , pp. 637-653 More about this Journal
Abstract
Rapid aging of the population affects population structure and population aging. Consequently, developed countries have focused on population aging as a major issue in regards to pension sustainability finances as well as health and the elderly welfare system. Mortality projections that result from population structure changes and population aging are increasingly important. This paper compares six mortality models using KOSTAT's life table from 1970 to 2016. The models are rooted in the Lee-Carter (LC) model (Lee and Carter, Journal of the American Statistical Association, 87, 659-671, 1992) and have been modified and improved on the assumptions of the LC model. We examined the improvement process and the check assumption by models in order to find a suitable mortality model for Korea. Korea shows rapid aging and declined mortality rate by age; therefore, it is desirable to estimate and predict mortality from LL&LC-ER models by combining LC-ER, LL, and LC-ER models that reflect the phenomena and modify age-specific mortality patterns without major changes in expected life expectancy.
Keywords
population aging; mortality model; Lee-Carter; LC-ER; LL&LC-ER;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 Bohk-Ewald, C., Ebeling, M., and Rau, R. (2017). Lifespan disparity as an additional indicator for evaluating mortality forecasts, Demography, 54, 1559-1577.   DOI
2 Booth, H., Maindonald, J., and Smith, L. (2002). Applying Lee-Carter under conditions of variable mortality decline, Population Studies, 56, 325-336.   DOI
3 Brillinger, D. R. (1986). The natural variability of vital rates and associated statistics, Biometrics, 42, 693-734.   DOI
4 Cairns, A., Blake, D., and Dowd, K. (2006). A two-factor model for stochastic mortality: theory and calibration, Journal of Risk and Insurance, 73, 687-718.   DOI
5 Cairns, A., Blake, D., Dowd, K., Coughlan, G., 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
6 Cairns, A., Blake, D., Dowd, K., Coughlan, G., Epstein, D., and Khalaf-Allah, M. (2011). Mortality density forecasts: an analysis of six stochastic mortality models, Insurance Mathematics and Economics, 43, 355-367.
7 De Jong, P. and Tickle, L. (2006). Extending Lee-Carter mortality forecasting, Mathematical Population Studies, 13, 1-18.   DOI
8 Kim, S. Y., Oh, J. H., and Kim, K. W. (2018). A comparison of mortality projection by different time period in time series, The Korean Journal of Applied Statistics, 31, 41-65.
9 Guibert, Q., Lopez, O., and Piette P. (2017). Forecasting mortality rate improvements with a high-dimensional VAR, HAL-01613050, doi:hal.archives-ouvertes.fr/hal-01613050
10 Doukhan, P., Pommeret, D., Rynkiewicz, J., and Salhi, Y. (2017). A class of random field memory models for mortality forecasting, Insurance : Mathematics and Economics, 77, 97-110.   DOI
11 Horiuchi, S. and Wilmoth, J. (1995). Annual meeting of the population association of America. San Francisco, CA: Population Association of America.
12 Hunt, A. and Villegas A. M. (2015). Robustness and convergence in the Lee-Carter model with cohort effects, Insurance: Mathematics and Economics, 64, 186-202.   DOI
13 Jeong, S. and Kim K. W. (2011). A Comparison study for mortality forecasting model by average life expectancy, The Korean Journal of Applied Statistics, 24, 115-125.   DOI
14 Jung, K., Back, J., and Kim, D. (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
15 Kannisto, V., Lauritsen, J., Thatcher, A. R., and Vaupel, J. W. (1994). Reductions in mortality at advanced ages: several decades of evidence from 27 countries, Population and Development Review, 20, 793-810.   DOI
16 Kang, J. C., Lee, J. C., and Sung, J. H. (2006). A study on methods for forecasting mortality considering longevity risk, The Journal of Risk Management, 17, 153-178.
17 Kim, S. J. (2012). A comparison study on the stochastic mortality models for measuring longevity risk, Korean Insurance Journal, 93, 213-235.
18 Lee, R. D. and Miller, T. (2001). Evaluating the performance of the Lee-Carter method for forecasting mortality, Demography, 38, 537-549.   DOI
19 KOSIS (2016). Population Projections (2015-2065).
20 Lee, R. D. and Carter, L. R. (1992). Modeling and forecasting U.S. mortality, Journal of the American Statistical Association, 87, 659-671.
21 Li, H. and Lu, Y. (2017). Coherent forecasting of mortality rates: a Sparse Vector-Autoregression approach, ASTIN Bulletin: The Journal of the IAA, 47, 563-600.   DOI
22 Li, N. and Gerland, P. (2011). Modifying the Lee-Carter Method to Project Mortality Changes up to 2100, the Population Association of America 2011 Annual meeting-Washington, DC, Session 125, formal Demography I: Mathematical Models and Methods.
23 Li, N. and Lee R. (2005). Coherent mortality forecasts for a group of populations: an extension of the Lee-Carter method, Demography, 42, 575-594.   DOI
24 Li, N., Lee, R., and Gerland, P. (2013). Extending the lee-carter method to model the rotation of age patterns of mortality decline for long-term projections, Demography, 50, 2037-2051.   DOI
25 Park, Y. S., Kim, K. W., Lee, D. H., and Lee, Y. K. (2005). A comparison of two model for forecasting mortality in South Korea, The Korean Journal of Applied Statistics, 18, 639-654.   DOI
26 Renshaw, A. E. 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
27 Statistics Canada (2015). Population Projections for Canada(2013 to 2063), Provinces and Territories (2013 to 2038): Technical Report on Methodology and Assumptions.
28 Kim, S. Y. and Han, M. J. (2017). Mortality forecasting for 2016 Korean Population Projection, Korean Journal of Population Studies, 40, 1-25.
29 Kim, S. Y. and Oh, J. H. (2017). A study comparison of mortality projection using parametric and nonparametric model, The Korean Journal of Applied Statistics, 30, 701-717.   DOI
30 Sevcikova, H., Li, N., Kantorova, V., Gerland, P., and Raftery, A. E. (2015). Age-Specific Mortality and Fertility Rates for Probabilistic Population Projections. Working paper no. 150, Center for Statistics and the Social Sciences University of Washington.
31 Thatcher, A. R., Kannisto, V., and Vaupel, J. W. (1998). The force of mortality at ages 80 to 120, Odense Monographs on Population Aging 5, Odense University Press.
32 Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net, Royal Statistical Society, 67, 301-320.   DOI