Sparse temporal disaggregation
Sparse temporal disaggregation
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DOI：
10.1111/rssa.12952
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
L. Mosley;I. Eckley;A. Gibberd
中科院分区：
文献类型：
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作者：
L. Mosley;I. Eckley;A. Gibberd
时间分类是官方统计中常用的一种方法，以实现对国内生产总值(GDP)等关键经济指标的高频估计。传统上，这种方法只依赖几个高频指标系列来产生估计。然而，大量的和不断增加的行政和替代数据来源促使这种方法适应高维度环境的需要。在本文中，我们提出了一种新的稀疏时间分解方法，并将其与经典的Chow-Lin方法进行了比较。我们通过仿真研究展示了我们提出的方法的性能，突出了实现的各种优势。我们还探索了它在英国GDP数据分解中的应用，展示了该方法在潜在指标数量大于低频观测数量时的操作能力。
Temporal disaggregation is a method commonly used in official statistics to enable high‐frequency estimates of key economic indicators, such as gross domestic product (GDP). Traditionally, such methods have relied on only a couple of high‐frequency indicator series to produce estimates. However, the prevalence of large, and increasing, volumes of administrative and alternative data‐sources motivates the need for such methods to be adapted for high‐dimensional settings. In this article, we propose a novel sparse temporal‐disaggregation procedure and contrast this with the classical Chow–Lin method. We demonstrate the performance of our proposed method through simulation study, highlighting various advantages realised. We also explore its application to disaggregation of UK GDP data, demonstrating the method's ability to operate when the number of potential indicators is greater than the number of low‐frequency observations.