Bayesian Forecasting of Cohort Fertility
Bayesian Forecasting of Cohort Fertility
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DOI：
10.1080/01621459.2014.881738
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发表时间：
2014-06-01
影响因子：
3.7
通讯作者：
Myrskylae, Mikko
中科院分区：
文献类型：
--
作者：
Schmertmann, Carl;Zagheni, Emilio;Myrskylae, Mikko
有迹象表明，富裕国家的生育率可能已经停止下降，但这关键取决于目前处于育龄的妇女是否会推迟或降低终生生育率。对平均完整家庭规模的分析需要预测 1970 年至 1995 年出生的女性的剩余生育能力。我们提出了一种生育率贝叶斯模型，其中包含有关年龄和时间模式的先验信息。我们使用新的数据集人类生育率数据库（HFD）来构建不正确的先验，为历史上合理的利率表面赋予较高的权重。在年龄维度上，队列计划应与 HFD 计划的主要组成部分非常接近。在时间维度上，序列应该是平滑的并且在短跨度内近似线性。我们校准先验，以便近似残差具有与历史 HFD 数据相似的理论分布。我们的先验使用二次惩罚并暗示每个国家的生育率表面的高维正态后验分布。对目前 15-44 岁 HFD 人群的预测显示出一致的模式。在美国、北欧和西欧，完成生育率可能会小幅反弹。在中欧、南欧、东亚和巴西，几乎没有反弹的证据。我们的方法只需稍作修改即可应用于其他预测和缺失数据问题。
There are signs that fertility in rich countries may have stopped declining, but this depends critically on whether women currently in reproductive ages are postponing or reducing lifetime fertility. Analysis of average completed family sizes requires forecasts of remaining fertility for women born 1970-1995. We propose a Bayesian model for fertility that incorporates a priori information about patterns over age and time. We use a new dataset, the Human Fertility Database (HFD), to construct improper priors that give high weight to historically plausible rate surfaces. In the age dimension, cohort schedules should be well approximated by principal components of HFD schedules. In the time dimension, series should be smooth and approximately linear over short spans. We calibrate priors so that approximation residuals have theoretical distributions similar to historical HFD data. Our priors use quadratic penalties and imply a high-dimensional normal posterior distribution for each country's fertility surface. Forecasts for HFD cohorts currently aged 15-44 show consistent patterns. In the United States, Northern Europe, and Western Europe, slight rebounds in completed fertility are likely. In Central and Southern Europe, East Asia, and Brazil, there is little evidence for a rebound. Our methods could be applied to other forecasting and missing-data problems with only minor modifications.