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시계열 이용기간에 따른 사망률 예측 비교

A comparison of mortality projection by different time period in time series

  • Kim, Soon-Young (Statistical Research Institute, Statistics Korea) ;
  • Oh, Jinho (Statistical Research Institute, Statistics Korea) ;
  • Kim, Kee-Whan (Department of Applied Statistics, Korea University)
  • 투고 : 2017.09.05
  • 심사 : 2017.12.15
  • 발행 : 2018.02.28

초록

우리나라의 경우 선진국에 비해 짧은 기간 동안 사망률 개선이 급속히 이루어짐에 따라 사망률 예측에 있어 모형의 선택뿐만 아니라 시계열 이용기간의 선정 또한 중요한 고려사항이 될 수 있다. 따라서 본 연구에서는 시계열 이용기간의 선택 관점에서 회귀모형을 이용하는 방법을 제안하였다. 또한 Lee-Carter (LC) 모형, LC류 (Lee-Miller (LM), Booth-Maindonald-Smith (BMS)) 그리고 비모수 모형(functional data model (FDM), Coherent FDM)을 토대로 시계열 이용기간을 다르게 적용할 경우 어떠한 문제가 발생되며, 연령별 사망률과 기대수명 예측력에 어떠한 차이를 보이는지 살펴보았다. 분석결과를 바탕으로 5개의 모형별 2030년까지 남녀의 연령별 사망률과 예측기대수명을 작성하고 통계청(Korean Statistical Information Service; KOSIS)에서 제공하는 장래 연령별 사망률과 기대수명과 비교하였다.

In Korea, as the mortality rate improves in a shorter period of time than in developed countries, it is important to consider the selection of the time series as well as the model selection in the mortality projection. Therefore, this study proposed a method using the multiple regression model in respect to the selection of the time series period. In addition, we investigate the problems that arise when various time series are used based on the Lee-Carter (LC) model, the kinds of LC model along with Lee-Miller (LM) and Booth-Maindonald-Smith (BMS), and the non-parametric model such as functional data model (FDM) and Coherent FDM, and examine differences in the age-specific mortality rate and life expectancy projection. Based on the analysis results, the age-specific mortality rate and predicted life expectancy of men and women are calculated for the year 2030 for each model. We also compare the mortality rate and life expectancy of the next generation provided by Korean Statistical Information Service (KOSIS).

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참고문헌

  1. Booth, H., Maindonald, J., and Smith, L. (2002). Applying Lee-Carter under conditions of variable mortality decline, Population Studies, 56, 325-336. https://doi.org/10.1080/00324720215935
  2. Cairns, A. J. G., Blake, D., Dowd, K., Coughlan, G. D., Epstein, D., Ong, A., and Balevich, I. (2009). A Quantitative comparison of stochastic mortality models using data from England and Wales and the US, North American Actuarial Journal, 13, 1-35. https://doi.org/10.1080/10920277.2009.10597538
  3. Hyndman, R. J. (2010), demography: Forecasting mortality, fertility, migration and population data. R package version 1.07. Contribution from Heather Booth and Leonie Tickle and John Maindonald.
  4. Hyndman, R. J. and Booth, H. (2008). Stochastic population forecasts using functional data models for mortality, fertility and migration, International Journal of Forecasting, 24, 323-342. https://doi.org/10.1016/j.ijforecast.2008.02.009
  5. Hyndman, R. J., Booth, H., and Yasmeen, F. (2013). Coherent mortality forecasting: the product-ratio method with functional time series models, Demography, 50, 261-283. https://doi.org/10.1007/s13524-012-0145-5
  6. Hyndman, R. J. and Ullah, S. (2007). Robust forecasting of mortality and fertility rates: a functional data approach, Computational Statistics & Data Analysis, 51, 4942-4956. https://doi.org/10.1016/j.csda.2006.07.028
  7. Jeong, S. and Kim, K. W. (2011). A comparison study for mortality forecasting models by average life expectancy, The Korean Journal of Applied Statistics, 24, 115-125. https://doi.org/10.5351/KJAS.2011.24.1.115
  8. 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. https://doi.org/10.5351/KJAS.2013.26.6.1019
  9. Jung, K. and Kim, D. (2012). An estimation of an old age mortality rate using CK model and relational model, Communications of the Korean Statistical Society, 19, 859-868.
  10. Kang, J. C., Lee, D. S., and Shung, J. H. (2006). A study on the methods for forecasting mortality considering longevity risk, The Journal of Risk Management, 17, 153-178.
  11. KOSIS (2016). Population Projections (2015-2065).
  12. Lee, R. D. and Carter, L. R. (1992). Modeling and forecasting U.S. mortality, Journal of the American Statistical Association, 87, 659-671.
  13. Lee, R. D. and Miller, T. (2001). Evaluating the performance of the Lee-Carter method for forecasting mortality, Demography, 38, 537-549. https://doi.org/10.1353/dem.2001.0036
  14. Park, K. A. (2011). Theory of practice of population projections, Statistics Training Institute.
  15. Park, Y. S., Kim, K. W., Lee, D. H., and Lee, Y. K. (2005). A comparison of two models for forecasting mortality in South Korea, The Korean Journal of Applied Statistics, 18, 639-654. https://doi.org/10.5351/KJAS.2005.18.3.639
  16. Swanson, D. A. and Beck, D. M. (1994). A new short-term county population projection method, Journal of Economic and Social Measurement, 20, 25-50.
  17. Tayman, J., Smith, S. K., and Lin, J. (2007). Precision, bias, and uncertainty for state population forecasts: An exploratory analysis of time series models, Population Research and Policy Review, 26, 347-369. https://doi.org/10.1007/s11113-007-9034-9
  18. UNPD (2015). World Population Projections 2015 Revision.