Combining individual- and population-level data to develop a Bayesian parity-specific fertility projection model
开发了一个结合个体和总体数据的贝叶斯胎次特异性生育率预测模型，应用于英格兰和威尔士，利用个体数据生成包含教育等变量的合理预测，对人口预测和公共服务规划有用。
Abstract 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 from Understanding 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.