BACKGROUND
Cohort-component models incorporating directional migration are conceptually robust demographic models which are widely employed to forecast the populations of large subnational regions. However, they are difficult to apply at the local area scale. Simpler models, such as the Hamilton–Perry model, have modest input data requirements and are much quicker, cheaper, and easier to implement, but they offer less output detail, suffer from some conceptual and practical limitations, and can be less accurate.

OBJECTIVE
The aim of this paper is to describe and evaluate the synthetic migration cohort-component model – an approach to implementing the bi-regional model for local area population forecasts without the need for any locally specific migration data.

METHODS
The new approach is evaluated by creating several sets of ‘forecasts’ for local areas of Australia over past periods. For comparison, forecasts from two types of Hamilton–Perry model are also evaluated. Error is measured primarily with an alternative Absolute Percentage Error measure for total population which takes into account how well or poorly the population age–sex structure is forecast.

RESULTS
In the evaluation for Australian local areas, the synthetic migration model generated more accurate forecasts that the two Hamilton–Perry models in terms of median, mean, and 90th percentile Absolute Percentage Errors.



中文翻译：

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使用双区域队列组件模型准备当地人口预测，而不需要当地迁移数据（汤姆威尔逊）

背景
包含定向迁移的队列组件模型在概念上是稳健的人口模型，广泛用于预测大型次国家区域的人口。但是，它们很难在局部范围内应用。更简单的模型，例如 Hamilton-Perry 模型，对输入数据的要求适中，并且更快、更便宜、更容易实现，但它们提供的输出细节更少，受到一些概念和实际限制，并且可能不太准确。

目的
本文的目的是描述和评估合成迁移队列组件模型——一种在不需要任何本地特定迁移数据的情况下实施本地人口预测的双区域模型的方法。

方法
新方法通过在过去时期为澳大利亚当地地区创建几组“预测”进行评估。为了比较，还评估了两种 Hamilton-Perry 模型的预测。误差主要通过总人口的替代绝对百分比误差度量来衡量，该度量考虑了人口年龄-性别结构预测的好坏。

结果
在对澳大利亚局部地区的评估中，综合迁移模型在中位数、平均值和 90% 绝对百分比误差方面产生了比两个 Hamilton-Perry 模型更准确的预测。