Forecasting of Cohort Fertility Under a Hierarchical Bayesian Approach
Forecasting of Cohort Fertility Under a Hierarchical Bayesian Approach
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分层贝叶斯方法下的队列生育率预测
DOI：
10.1111/rssa.12566
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Ellison J
中科院分区：
文献类型：
--
作者：
Ellison J
生育率预测是人口预测的关键决定因素，政府政策制定者和规划者广泛使用人口预测。为了与最近的文献保持一致，我们提出了一个直观和透明的分层贝叶斯模型来预测队列生育率。使用哈密顿蒙特卡罗方法和来自人类生育力数据库的数据集，我们得到了30个国家的生育率预测。我们使用评分规则来定量评估预测的预测准确性；这表明我们的模型预测的准确性与当前文献中表现最好的模型的准确性相当，对于没有最近结构性转变的国家来说，预测的表现更好。我们的发现支持了分层贝叶斯模型在人口预测方法中的领先地位。
Fertility projections are a key determinant of population forecasts, which are widely used by government policy makers and planners. In keeping with the recent literature, we propose an intuitive and transparent hierarchical Bayesian model to forecast cohort fertility. Using Hamiltonian Monte Carlo methods and a data set from the human fertility database, we obtain fertility forecasts for 30 countries. We use scoring rules to assess the predictive accuracy of the forecasts quantitatively; these indicate that our model predicts with an accuracy comparable with that of the best-performing models in the current literature overall, with stronger performance for countries without a recent structural shift. Our findings support the position of hierarchical Bayesian modelling at the forefront of population forecasting methods.