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

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 generalized 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 optimize the model over 14 monthly GDP data releases from the UK Office for National Statistics. Our GNAR-ex model can outperform baseline autoregressive benchmark models and dynamic factor models with respect to 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. This paper contributes 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.