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

实时了解社会：经济建模的联合临近预报和分解方法

• 批准号: 2866675
• 金额: --
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2866675
• 关键词: Understanding; Society; Real; time; Joint

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.

引言该项目旨在与国家统计局（ONS）合作开发方法论，以通过使用统计模型以面向时期的方式吸收信息来帮助我们跟踪社会的变化。调查两个示例，我们可以在一个区域级别上观察到每年一次的每年一次，我们只能在每年的地区观察GDP，以便每年一次求解GDP，我们只能在每个领域进行gdp of for gdp of for GD，而我们却始于每年一次的gd for gde，for gde for gde是一分之一的，并且是gd for gde的，并且我们只能在gdementive of aftererive。在所有季度服务中，所有服务都希望以每月的水平分类为“部门”的贸易。这两种情况都要求我们以较高的分辨率估算我们罐头观察到的更高分辨率，前者增加时间分辨率的时间序列，后者增加了粒度（骨料到核心部门）和时间（季度到月份）的分辨率。至关重要的是，我们希望实时估算行为，以便在收集任何相关数据时，将更新输出估算。这种经济预测至关重要，并且在做出政策制定决策时会产生影响，例如，针对基础架构资金，调整监管，口头上的经济工具，例如利率。 Al。，2020）倾向于解决一个或另一个问题，而不是共同解决。此外，考虑到如今可用的数据源范围（数百个），我们可能以高于我们希望描述的输出系列的分辨率可用的相关信息可用，例如，我们可能有财务时间序列可以提供每日级别的信息，或者可以提供一个可以在公路上提供的信息，并且可以提供较高的临时信息，并且可以分配较高的时间。动画挑战是如何将此类数据纳入AffirateManner中的统计模型。