GDP nowcasting with large-scale inter-industry payment data in real time -- A network approach

TL;DR

This paper introduces a novel network-based model, GNAR-ex, for real-time GDP nowcasting using inter-industry payment data, outperforming traditional methods and providing timely, granular economic estimates.

Contribution

It develops the first network approach for GDP nowcasting using payment data and extends the GNAR model to handle time-varying edges and nodal series.

Findings

GNAR-ex outperforms baseline autoregressive models in forecasting accuracy.

The model provides robust GDP nowcasts across multiple data revisions.

Application to UK payment data demonstrates practical effectiveness.

Abstract

Real-time economic information is essential for policy-making but difficult to obtain. We introduce a granular nowcasting method for macro- and industry-level GDP using a network approach and data on real-time monthly inter-industry payments in the UK. To this purpose we devise a model which we call an extended generalised network autoregressive (GNAR-ex) model, tailored for networks with time-varying edge weights and nodal time series, that exploits the notion of neighbouring nodes and neighbouring edges. The performance of the model is illustrated on a range of synthetic data experiments. We implement the GNAR-ex model on the payments network including time series information of GDP and payment amounts. To obtain robustness against statistical revisions, we optimise the model over 9 quarterly releases of GDP data from the UK Office for National Statistics. Our GNAR-ex model can outperform baseline autoregressive benchmark models, leading to a reduced forecasting error. This work helps to obtain timely GDP estimates at the aggregate and industry level derived from alternative data sources compared to existing, mostly survey-based, methods. Thus, this paper contributes both, a novel model for networks with nodal time series and time-varying edge weights, and the first network-based approach for GDP nowcasting based on payments data.