Wiśniowski Arkadiusz, Raymer James
Social Statistics Department, University of Manchester, Oxford Rd, Manchester, M13 9PL, UK.
School of Demography, Australian National University, 146 Ellery Crescent, Acton, ACT, 2601, Australia.
Eur J Popul. 2025 Apr 10;41(1):11. doi: 10.1007/s10680-025-09729-7.
In this article, we extend the multiregional cohort-component population projection model developed by Andrei Rogers and colleagues in the 1960s and 1970s to be fully probabilistic. The projections are based on forecasts of age-, sex- and region-specific fertility, mortality, interregional migration, immigration and emigration. The approach is unified by forecasting each demographic component of change by using a combination of log-linear models with bilinear terms. This research contributes to the literature by providing a flexible statistical modelling framework capable of incorporating the high dimensionality of the demographic components over time. The models also account for correlations across age, sex, regions and time. The result is a consistent and robust modelling platform for forecasting subnational populations with measures of uncertainty. We apply the model to forecast population for eight states and territories in Australia.
在本文中，我们将安德烈·罗杰斯及其同事在20世纪60年代和70年代开发的多区域队列成分人口预测模型扩展为完全概率性的模型。这些预测基于对特定年龄、性别和地区的生育率、死亡率、区域间迁移、移民和出境移民的预测。该方法通过使用带有双线性项的对数线性模型组合来预测每个变化的人口组成部分，从而实现统一。本研究通过提供一个灵活的统计建模框架为文献做出了贡献，该框架能够纳入人口组成部分随时间的高维度特征。这些模型还考虑了年龄、性别、地区和时间之间的相关性。结果是一个用于预测次国家人口并带有不确定性度量的一致且稳健的建模平台。我们应用该模型对澳大利亚的八个州和领地的人口进行预测。
