评估汉密尔顿-佩里模型在小区域人口预测中的替代实施:以澳大利亚为例

IF 1.1 Q3 DEMOGRAPHY
Tom Wilson, Irina Grossman
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引用次数: 3

摘要

小区域人口预测广泛应用于公共和私营部门,许多用户需要按性别和年龄组进行预测。利用多区域队列组成模型编制整个国家或州的小区域年龄-性别人口预测通常是一项耗时和昂贵的任务。它涉及购买大型数据集,大量复杂的数据准备和假设设置,以及大量的工作人员时间。一种更快、成本更低的替代方法是使用简化的队列预测模型,比如汉密尔顿-佩里模型。本文对Hamilton-Perry模型的各种实现进行了评估,包括采用队列变化比率和队列变化差异组合的替代版本。本文还评估了平滑队列变化率和差异的年龄特征以及约束独立人口预测对预测准确性的影响。人口“预测”是为2006 - 2016年期间澳大利亚的小区域创建的,并与人口估计值进行比较。最准确的实现被发现是Hamilton-Perry模型,它结合了队列变化比率和队列变化差异,平滑的年龄概况,并约束独立预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating Alternative Implementations of the Hamilton-Perry Model for Small Area Population Forecasts: the Case of Australia

Small area population forecasts are widely used across the public and private sectors, with many users requiring forecasts broken down by sex and age group. The preparation of small area age-sex population forecasts across a whole country or State with a multiregional cohort-component model is usually a time-consuming and expensive task. It involves the purchase of large datasets, considerable amounts of complex data preparation and assumption-setting, and substantial amounts of staff time. A quicker and lower-cost alternative is to use a reduced form cohort projection model, such as the Hamilton-Perry model. This paper presents an evaluation of various implementations of the Hamilton-Perry model, including an alternative version employing a combination of Cohort Change Ratios and Cohort Change Differences. It also evaluates the effects on forecast accuracy of smoothing the age profiles of Cohort Change Ratios and Differences, and constraining to independent population forecasts. Population ‘forecasts’ were created for small areas of Australia over the horizon 2006–16 and compared against population estimates. The most accurate implementation is found to be the Hamilton-Perry model using a combination of Cohort Change Ratios and Cohort Change Differences, smoothed age profiles, and with constraining to independent forecasts.

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来源期刊
Spatial Demography
Spatial Demography DEMOGRAPHY-
自引率
0.00%
发文量
12
期刊介绍: Spatial Demography focuses on understanding the spatial and spatiotemporal dimension of demographic processes.  More specifically, the journal is interested in submissions that include the innovative use and adoption of spatial concepts, geospatial data, spatial technologies, and spatial analytic methods that further our understanding of demographic and policy-related related questions. The journal publishes both substantive and methodological papers from across the discipline of demography and its related fields (including economics, geography, sociology, anthropology, environmental science) and in applications ranging from local to global scale. In addition to research articles the journal will consider for publication review essays, book reviews, and reports/reviews on data, software, and instructional resources.
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