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

Introduction
This 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 a
real-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 produce
estimates for GDP each quarter and for each region.
2) We observe trade flows aggregated across all services at a quarterly level-we wish to
estimate 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 can
natively observe, the former increasing the temporal resolution, and the latter increasing both the
granularity (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, the
output estimate will be updated. Economic forecasts of this kind are vital and find impact when
feeding into policy making decisions, e.g., targeting infrastructure funding, adjusting regulation, or
altering economic instruments such as interest rates.
These tasks require us to combine methodological paradigms surrounding disaggregation, and
nowcasting-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 the
wide range (hundreds) of data-sources nowadays availiable, it is likely that we may have relevant
information 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 datasets
such as road-traffic counts that can exhibit both high temporal and spatial resolution. An
important challenge is how to incorporate such data into statistical models in an appropriate
manner.