Combining individual- and population-level data to develop a Bayesian parity-specific fertility projection model
Combining individual- and population-level data to develop a Bayesian parity-specific fertility projection model
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DOI：
10.1093/jrsssc/qlad095
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发表时间：
2023-11-03
影响因子：
1.6
通讯作者：
Forster，Jonathan J.
中科院分区：
文献类型：
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作者：
Ellison，Joanne;Berrington，Ann;Forster，Jonathan J.
生育率预测对于预测孕产妇和儿童保育服务等方面的需求至关重要。模型通常只使用总体水平的数据，而忽略了个体水平数据的丰富性。因此，我们开发了一个贝叶斯奇偶性特定的投影模型结合这些数据源。我们将我们的方法应用于英格兰和威尔士，使用来自理解社会的个人层面数据。拟合广义加性模型可以平滑地预测年龄、队列和自上次出生以来的时间。我们还纳入了有关数据源的相对重要性的先验信念。我们的方法产生合理的预测个人层面的变量，包括教育资格，尽管他们在人口水平的数据。
Fertility projections are vital to anticipate demand for maternity and childcare services, among other uses. Models typically use aggregate population-level data alone, ignoring the richness of individual-level data. We hence develop a Bayesian parity-specific projection model combining such data sources. We apply our method to England and Wales, using individual-level data fromUnderstanding Society. Fitting generalised additive models gives smooth projections across age, cohort, and time since last birth. We also incorporate prior beliefs about the relative importance of the data sources. Our approach generates plausible forecasts by individual-level variables including educational qualification, despite their absence in the population-level data.