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Temporal disaggregation and restricted forecasting of multiple population time series

机译:多个人口时间序列的时间分解和受限预测

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This article presents some applications of time-series procedures to solve two typical problems that arise when analyzing demographic information in developing countries: (1) unavailability of annual time series of population growth rates (PGRs) and their corresponding population time series and (2) inappropriately defined population growth goals in official population programs. These problems are considered as situations that require combining information of population time series. Firstly, we suggest the use of temporal disaggregation techniques to combine census data with vital statistics information in order to estimate annual PGRs. Secondly, we apply multiple restricted forecasting to combine the official targets on future PGRs with the disaggregated series. Then, we propose a mechanism to evaluate the compatibility of the demographic goals with the annual data. We apply the aforementioned procedures to data of the Mexico City Metropolitan Zone divided by concentric rings and conclude that the targets established in the official program are not feasible. Hence, we derive future PGRs that are both in line with the official targets and with the historical demographic behavior. We conclude that growth population programs should be based on this kind of analysis to be supported empirically. So, through specialized multivariate time-series techniques, we propose to obtain first an optimal estimate of a disaggregate vector of population time series and then, produce restricted forecasts in agreement with some data-based population policies here derived.
机译:本文介绍了时间序列程序的一些应用,以解决分析发展中国家的人口统计信息时出现的两个典型问题:(1)无法获得人口增长率(PGR)的年度时间序列及其相应的人口时间序列,以及(2)官方人口方案中人口增长目标定义不当。这些问题被认为是需要组合人口时间序列信息的情况。首先,我们建议使用时间分解技术将普查数据与人口动态统计信息结合起来,以估算年度PGR。其次,我们应用多重受限预测,将未来PGR的官方目标与分类序列相结合。然后,我们提出了一种机制来评估人口统计目标与年度数据的兼容性。我们将上述程序应用于以同心环划分的墨西哥城大都会区的数据,并得出结论认为,官方程序中建立的目标不可行。因此,我们得出了既符合官方目标又符合历史人口行为的未来PGR。我们得出结论,增长人口计划应基于这种分析,并得到经验支持。因此,通过专门的多元时间序列技术,我们建议首先获得人口时间序列分类向量的最佳估计,然后与此处导出的一些基于数据的人口政策相一致地产生受限的预测。

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