Modeling and evaluating longevity risk in consideration with long term care status
| Project/Area Number |
25380403
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Money/ Finance
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| Research Institution | Keio University |
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Principal Investigator |
KOGURE Atsuyuki 慶應義塾大学, 総合政策学部(藤沢), 教授 (80178251)
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| Project Period (FY) |
2013-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2015: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2014: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2013: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 介護リスク / 長寿リスク / 確率的死亡率モデル / ベイズ / アクチュアリアル・サイエンス / 要介護状態 / ベイズアプローチ / 介護保険 / モデリング / 評価 / ベイズ・アプローチ / 介護 |
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| Outline of Final Research Achievements |
Increased human lifetime is accompanied by a greater chance of becoming disabled. Aging populations such as Japan have faced with the so-called long-term care (LTC) risk in addition to the longevity risk. The key element of the solutions is how the mortality is related to health states. However, study on the complicated mortality dynamics between the mortality and the health state is limited due to lack of data. This research has proposed a new methodology to forecast mortality rates by the LTC status under the premise that the death data on the LTC sub-populations are not available, but the corresponding population exposures are available. Based on this model we have constructed a Bayesian methodology to forecast future mortality rates. We have applied the proposed methodology to the data from the Japanese long-term care insurance system and predicted the future mortality rates by the required level of the long-term care.
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Report
(5 results)
Research Products
(19 results)
