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Longevity advances in high-income countries, 1955-96

机译:1955-96年高收入国家的长寿发展

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Lee and Carter (1992) have argued that the best historical predictor of change in American mortality from 1933 to 1987 is a function that generates linear projections of logged age-specific death rates. While their model allowed for a variable rateof mortality change over time, they found that the best fit involved an almost perfectly linear model. As Manton, Stallard, and Tolley (1991) have shown, many expert predictions of future change made in the course of the twentieth century were later proven to be wildly wrong, but 92.7 percent of the variance in age-specific log death rates over time can be explained by straight-line projections (Lee and Carter 1992; Lee and Miller 2001). Since Lee and Carter's article, advocates of historical projectionhave rallied to the use of logged age-specific death rates to predict future trends (Preston 1993; Wilmoth 1998a, 1998b; Tuljapurkar, Li, and Boe 2000). This article expands on this empirical work by asking: Is linear change in logged age-specific deathrates the empirical model that best describes change over the more recent period, 1955-96? If not, what simple trend has been the best historical predictor, and hence a good basis for future projections? Does the pattern found in the United States holdfor other countries whose economic and social development has been most similar? And what does the experience of the group of developed countries imply for the future of individual states?
机译:Lee和Carter(1992)认为,从1933年到1987年,美国死亡率变化的最好的历史预测指标是一个函数,该函数可以生成对数特定年龄死亡率的线性预测。尽管他们的模型允许随时间变化的死亡率变化率,但他们发现最佳拟合涉及几乎完美的线性模型。正如Manton,Stallard和Tolley(1991)所表明的那样,许多专家对二十世纪未来变化的预测后来被证明是错误的,但是随着时间的推移,特定年龄段对数死亡率的变化为92.7%。可以用直线投影来解释(Lee和Carter 1992; Lee和Miller 2001)。自从Lee和Carter发表文章以来,历史预测的提倡者一直使用记录的特定年龄死亡率来预测未来趋势(Preston 1993; Wilmoth 1998a,1998b; Tuljapurkar,Li和Boe 2000)。本文通过询问以下经验扩展了这一经验工作:记录的特定年龄死亡率的线性变化是否最能准确描述最近1955-96年变化的经验模型?如果不是,哪种简单趋势是最好的历史预测指标,从而为将来的预测奠定了良好的基础?美国发现的模式是否适用于其他经济和社会发展最相似的国家?发达国家集团的经验对每个国家的未来意味着什么?

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