[논문]코호트 효과를 고려한 확률적 사망률 예측 모형의 비교 연구

김순영

doi:10.5351/kjas.2021.34.3.347

코호트 효과를 고려한 확률적 사망률 예측 모형의 비교 연구
A comparative study of stochastic mortality models considering cohort effects 원문보기

응용통계연구 = The Korean journal of applied statistics, v.34 no.3, 2021년, pp.347 - 373

초록
AI-Helper

지난 50여 년 동안 우리나라의 사망률 감소 패턴에 대한 탐색적 연구에 의하면 연령별 사망률이 모든 연령에서 감소했지만, 특정한 사망률이 개선되고 있는 패턴은 연령과 기간에 따라 다르다는 것을 알 수 있다. 여자가 남자보다 사망률 개선이 뚜렷하고 특히 시간이 지나면서 특정그룹에서의 사망률 개선이 두드러짐에 따라 전반적으로 사망 시간 추세에 구조적인 변화가 존재함을 확인하였다. 이에 본 연구에서는 우리나라 여자 사망률 자료를 이용하여 미래 사망률 예측을 위해 코호트 효과를 고려한 다양한 확률적 사망률 모형을 살펴보았다. 또한 분석 결과를 바탕으로 2067년까지 연령별 사망률과 예측기대수명을 작성하고 통계청(KOSIS)에서 제공하는 장래 연령별 사망률과 기대수명과 비교하였다. 자료이용기간에 따라 최적의 모형이 상이하나 적합력과 예측력을 전반적으로 고려했을 때 우리나라 여자 사망률은 코호트 효과를 고려한 PLAT 모형이 적절하다 볼 수 있을 것이다.

Abstract ▼ AI-Helper

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.

주제어

표/그림 (21)

그림 Figure 1: Relationship between the age of logit-mortality and log-mortality.
그림 Figure 2: Age-groups log death rates for males and females over time (1970∼2018).
그림 Figure 3: Age-speciﬁc rates by date of birth (cohort death rate).
그림 Figure 4: Cohort-speciﬁc rates by age.
그림 Figure 5: Residual surface plot of the Lee-Carter model.
그림 Figure 6: Surface plot of smoothed mortality improvement rates.
표 Table 1: Formula for the stochastic mortality models
표 Table 2: Fitting and forecast period of modeling
표 Table 3: MAE of model by ﬁtting period and age
그림 Figure 7: Fitting age-speciﬁc log mortality (1983∼2010).
그림 Figure 8: Forecast age-speciﬁc log mortality (ﬁtting period 1983∼2010, forecast period 2011∼2018).
표 Table 4: Prediction method for period eﬀect and cohort eﬀect
표 Table 5: MFE of age-speciﬁc mortality (forecast period 2011∼2018)
표 Table 6: MAFE of age-speciﬁc mortality(forecast period 2011∼2018)
그림 Figure 9: Forecast life expectancy at birth (ﬁtting period 1983-2010, forecast period, 2011∼2018).
표 Table 7: MFE of life expectancy (forecast period 2011∼2018)
표 Table 8: MAFE of life expectancy at birth (forecast period 2011∼2018)
그림 Figure 10: Prediction life expectancy at birth from the six models(∼2067).
표 Table 9: Prediction life expectancy at birth from the six models (ﬁtting period 1970∼2018, age 0∼100+ prediction method 1)
표 Table 10: MFE and MAFE of age-speciﬁc mortality (2019∼2067)
표 Table 11: MFE and MAFE of life expectancy at birth(2019∼2067)

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