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Model Selection for High-Dimensional Temporal Disaggregation in Official Statistics

官方统计中高维时间分解的模型选择

基本信息

批准号：
ES/V006339/1
负责人：
Alexander Gibberd
金额：
$ 18.96万
依托单位：
Lancaster University
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=ES%2FV006339%2F1
关键词：
Model Selection Dimensional Temporal Disaggregation

项目摘要

Traditional methods for producing economics statistics, for instance GDP, rely on data gathered through surveys of a population. Whilst such methods are accurate, and well calibrated, they are very expensive to run, and take a long time to feed-back information. As such, National Statistics Institutes such as the UK's Office for National Statistics (ONS) are looking to integrate so-called administrative data, and alternative data-streams such as web-scrapped data into their estimation of economic statistics. Using such data can potentially increase both the frequency and the accuracy at which economic statistics are produced. However, it is often unclear how these alternative data-sources (of which there can be many) relate to the traditional survey results, and how we can produce high-frequency series which are consistent with the survey data.Given that we could measure many different aspects of the population, only a few of these might actually be relevant to producing a particular statistic of interest. From a methodological viewpoint, this mandates that we choose between several competing statistical models, a problem known as model selection. Traditional model selection methods assume that the number of data-points is much larger than the number of data-streams, however, when linking administrative, and alternative data-sources, that assumption will no longer hold and one has to consider the so-called high-dimensional statistical setting. This project proposes to adapt recent advances in high-dimensional methodology to the analysis and production of bench-marked economic statistics. The project aims to examine both the empirical behaviour of these methods via simulation, and work with practitioners at the ONS to implement and test these methods through the development of a easy to use software package.

编制经济统计数据的传统方法，例如国内生产总值，依赖于通过人口调查收集的数据。虽然这些方法是准确的，并且校准良好，但它们运行起来非常昂贵，并且需要很长时间来反馈信息。因此，英国国家统计局（ONS）等国家统计机构正在寻求将所谓的行政数据和替代数据流（如网络废弃数据）整合到其经济统计数据中。使用这些数据有可能提高编制经济统计数据的频率和准确性。然而，这些替代数据来源（可能有很多）与传统统计调查结果的关系，以及我们如何制作与统计调查数据一致的高频数列，往往并不清楚。由于我们可以衡量人口的许多不同方面，只有其中几个可能实际上与制作特定的统计数据有关。从方法论的角度来看，这要求我们在几个相互竞争的统计模型之间进行选择，这个问题被称为模型选择。传统的模型选择方法假设数据点的数量远大于数据流的数量，然而，当连接行政和替代数据源时，这种假设将不再成立，必须考虑所谓的高维统计设置。该项目提议将高维方法的最新进展用于分析和编制基准经济统计数据。该项目旨在通过模拟研究这些方法的经验行为，并与国家统计局的从业人员合作，通过开发易于使用的软件包来实施和测试这些方法。