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将 Lee-Carter 方法扩展到模型死亡率下降的年龄模式旋转,以进行长期预测。

Extending the Lee-carter method to model the rotation of age patterns of mortality decline for long-term projections.

机构信息

Population Division, United Nations, 2 UN Plaza-DC2-1914, New York, NY, 10017, USA,

出版信息

Demography. 2013 Dec;50(6):2037-51. doi: 10.1007/s13524-013-0232-2.

DOI:10.1007/s13524-013-0232-2
PMID:23904392
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4550589/
Abstract

In developed countries, mortality decline is decelerating at younger ages and accelerating at old ages, a phenomenon we call "rotation." We expect that this rotation will also occur in developing countries as they attain high life expectancies. But the rotation is subtle and has proved difficult to handle in mortality models that include all age groups. Without taking it into account, however, long-term mortality projections will produce questionable results. We simplify the problem by focusing on the relative magnitude of death rates at two ages (0 and 15-19) while making assumptions about changes in rates of decline at other ages. We extend the Lee-Carter method to incorporate this subtle rotation in projection. We suggest that the extended Lee-Carter method could provide plausible projections of the age pattern of mortality for populations, including those that currently have very high life expectancies. Detailed examples are given using data from Japan and the United States.

摘要

在发达国家,死亡率的下降在较年轻的年龄组中正在减缓,而在较老的年龄组中则在加速,这种现象我们称之为“旋转”。我们预计,随着发展中国家预期寿命的提高,这种旋转也将在发展中国家中出现。但是,这种旋转是微妙的,并且已经证明,在包括所有年龄组的死亡率模型中很难处理。如果不考虑这种旋转,长期死亡率预测将产生有问题的结果。我们通过关注两个年龄组(0 岁和 15-19 岁)的死亡率相对大小来简化问题,同时对其他年龄组的下降率变化做出假设。我们将 Lee-Carter 方法扩展到预测中,以纳入这种微妙的旋转。我们建议,扩展的 Lee-Carter 方法可以为包括目前预期寿命非常高的人群在内的人群提供死亡率年龄模式的合理预测。使用来自日本和美国的数据给出了详细的示例。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f2/4550589/6adf3f534123/nihms716485f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f2/4550589/942042d4394b/nihms716485f1.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f2/4550589/f7035859178b/nihms716485f6.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f2/4550589/d96eb96fc3c5/nihms716485f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f2/4550589/6adf3f534123/nihms716485f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f2/4550589/942042d4394b/nihms716485f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f2/4550589/f7cffe17c108/nihms716485f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f2/4550589/5a31d3dad4c9/nihms716485f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f2/4550589/e3b4613e8232/nihms716485f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f2/4550589/fd486f4e4901/nihms716485f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f2/4550589/f7035859178b/nihms716485f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f2/4550589/edd1c0aa2621/nihms716485f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f2/4550589/a1b664954430/nihms716485f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f2/4550589/d96eb96fc3c5/nihms716485f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/16f2/4550589/6adf3f534123/nihms716485f10.jpg

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