Nowcasting with payments system data

Nowcasting economic activity with electronic payments data: A predictive modeling approach

“…Evans (2005) and Giannonne et al (2008) are among the first and most influential works on nowcasting economic activity. Our work is closely related to that of Galbraith and Tkacz (2017), who claim to be the first to make economic activity nowcasting based on payments data. However, there are several differences with Galbraith and Tkacz (2017) as we (i) use a broader and larger spectrum of payment instruments that incorporates expenditures from individuals, firms, and the central government; (ii) implement a Nonlinear AutoRegressive eXogenous Artificial Neural Network model (NARX-ANN) that is documented for its predictive high-performance and flexibility; and (iii) attempt to nowcast economic activity with a model that does not include traditional inputs (e.g.…”

“…We choose electronic payment instruments data as our inputs for nowcasting economic activity. Our choice is unusual but has been already put into practice by Galbraith and Tkacz (2017). Galbraith and Tkacz use a set of electronic payments data comprising the value and number of operations from debit card transactions (i.e.…”

“…Second, from cheques that are processed and cleared in the local cheque clearing house, Cedec. Unlike Galbraith and Tkacz (2017), we do not consider debit and credit card transactions because their contribution to the total value of electronic payments is rather low (i.e. below 5 percent), and because data is available with a lag that is incompatible with a nowcasting task, i.e.…”

“…The lack of timely information about the current state of the economy is a well-recognized problem among policy makers (Evans, 2005). That explains why nowcasting, defined as current-period estimates (Galbraith & Tkacz, 2017) or the prediction of the present, the very near future and the very recent past (Bańbura et al, 2017), has become a standard activity for central banks (see Tiffin, 2016, Hinds et al, 2017.…”

Using electronic transaction data to add geographic granularity to official estimates of retail sales

“…For example, there is a growing literature on using electronic data in nowcasting models to make economic forecasts for the recent past, present, or near future. Galbraith and Tkacz [11] use monthly aggregates of the value and number of debit and check transactions to nowcast change in the Canadian gross domestic product and in retail sales. In a similar vein, D' Amuri and Marcucci [3] use Google job-search activity to nowcast the monthly unemployment rate in the United States.…”