Model Selection for High-Dimensional Temporal Disaggregation in Official Statistics

生产GDP等经济统计数据的传统方法依赖于通过人口调查收集的数据。虽然这些方法是准确的，而且校准得很好，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其他数据流（如网络废弃数据）整合到它们对经济统计的估计中。使用这些数据可以潜在地提高经济统计数据产生的频率和准确性。然而，通常不清楚这些替代数据源（可能有很多）如何与传统的调查结果相关联，以及我们如何产生与调查数据一致的高频序列。考虑到我们可以测量人口的许多不同方面，其中只有少数可能与产生感兴趣的特定统计数据有关。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远大于数据流的数量，然而，当连接管理数据源和替代数据源时，该假设将不再成立，必须考虑所谓的高维统计设置。该项目建议将高维方法的最新进展应用于基准经济统计数据的分析和编制。该项目旨在通过模拟来检验这些方法的经验行为，并与国家统计局的从业者合作，通过开发一个易于使用的软件包来实施和测试这些方法。

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.