Macroeconomic Predictions Using Payments Data and Machine Learning

Abstract

This paper assesses the usefulness of comprehensive payments data for macroeconomic predictions in Canada. Specifically, we evaluate which type of payments data are useful, when they are useful, why they are useful, and whether machine learning (ML) models enhance their predictive value. We find payments data with a factor model can help improve accuracy up to 25% in predicting GDP, retail, and wholesale sales; and nonlinear ML models can further improve the accuracy up to 20%. Furthermore, we find the retail payments data are more useful than the data from the wholesale system; and they add more value during crisis and at the nowcasting horizon due to the timeliness. The contribution of the payments data and ML models is small and linear during low and normal economic growth periods. However, their contribution is large, asymmetrical, and nonlinear during crises such as COVID-19. Moreover, we propose a cross-validation approach to mitigate overfitting and use tools to overcome interpretability in the ML models to improve their effectiveness for policy use.

Keywords

• nowcasting
• payments data
• machine learning
• interpretability
• overfitting