Understanding Society in Real-time: A Joint Nowcasting and Disaggregation Approach to Economic Modelling

本项目旨在与英国国家统计局(ONS)合作开发方法，帮助我们通过使用统计模型实时吸收信息来跟踪社会的变化。考虑两个我们试图提高对经济理解的分辨率的例子：1)我们在区域一级观察GDP，但每年只观察一次--我们希望对每个季度和每个区域的GDP进行估计。2)我们在季度水平上观察所有服务业的贸易流动--我们希望在月度水平上估计非聚集“部门”的贸易。这两种情况都要求我们以我们可以观察到的更高的分辨率来估计感兴趣的时间序列，前者提高了时间分辨率，后者既提高了粒度(合计到子部门)，又增加了时间(季度到每月)分辨率。至关重要的是，我们希望实时估计行为，以便在收集到任何相关数据时，输出估计值将被更新。这类经济预测是至关重要的，并在为决策提供信息时产生影响，例如，瞄准基础设施资金、调整监管、口头改变利率等经济工具。这些任务要求我们结合围绕分类的方法论范式，以及现在的预测--然而，现有方法(例如Banburra等人，2010；Proietti，2006；Koop等人，2020)倾向于解决其中一个问题，而不是同时解决两者。此外，考虑到目前可用的数据源范围很广(数百个)，我们很可能拥有比我们希望描述的输出序列更高的分辨率的相关信息，例如，我们可能有可以提供每日水平信息的金融时间序列，或者可以显示高时间和空间分辨率的区域数据集，例如道路交通计数。一个重要的挑战是如何将这些数据纳入适当的统计模型中。

IntroductionThis project aims to develop methodology in partnership with the Office for National Statistics(ONS) to help us track changes in society by using statistical models to assimilate information in areal-time fashion.Consider two examples where we try to enhance the resolution of our economic understanding:1) We observe GDP at a regional level, but only once per year-we wish to produceestimates for GDP each quarter and for each region.2) We observe trade flows aggregated across all services at a quarterly level-we wish toestimate trade for disaggregate "sectors" at a monthly level. Both these cases require us to estimate a time-series of interest at a higher resolution that we cannatively observe, the former increasing the temporal resolution, and the latter increasing both thegranularity (aggregate-to-sub-sector) and temporal (quarterly-to-monthly) resolution. Crucially,we desire to estimate the behaviour in real-time such that as any relevant data is collected, theoutput estimate will be updated. Economic forecasts of this kind are vital and find impact whenfeeding into policy making decisions, e.g., targeting infrastructure funding, adjusting regulation, oraltering economic instruments such as interest rates.These tasks require us to combine methodological paradigms surrounding disaggregation, andnowcasting-however existing methods (e.g. Banburra et al., 2010; Proietti, 2006; Koop et al.,2020) tend to tackle one or the other problem, not both jointly. Furthermore, considering thewide range (hundreds) of data-sources nowadays availiable, it is likely that we may have relevantinformation availiable to us at a resolution higher than the output series we wish to describe, e.g.,we may have financial time-series that can give information on a daily level, or regional datasetssuch as road-traffic counts that can exhibit both high temporal and spatial resolution. Animportant challenge is how to incorporate such data into statistical models in an appropriatemanner.