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.

中文翻译：

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分级贝叶斯方法下的队列生育率预测

生育率预测是人口预测的关键因素，政府决策者和计划者广泛使用该预测。与最新文献保持一致，我们提出了一种直观且透明的分层贝叶斯模型来预测队列生育率。使用汉密尔顿蒙特卡洛方法和人类生育率数据库的数据集，我们获得了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；这些表明，我们的模型所预测的准确度可与当前所有文献中表现最佳的模型相媲美，对于近期没有结构性变化的国家而言，其表现更为出色。我们的发现支持层次贝叶斯建模在人口预测方法的最前沿。