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Staff working papers

Macroeconomic Predictions Using Payments Data and Machine Learning

Staff Working Paper 2022-10 James Chapman, Ajit Desai
We demonstrate the usefulness of payment systems data and machine learning models for macroeconomic predictions and provide a set of econometric tools to overcome associated challenges.
Content Type(s): Staff research, Staff working papers Topic(s): Business fluctuations and cycles, Econometric and statistical methods, Payment clearing and settlement systems JEL Code(s): C, C5, C53, C55, E, E3, E37, E4, E42, E5, E52

Estimating Policy Functions in Payments Systems Using Reinforcement Learning

Staff Working Paper 2021-7 Pablo S. Castro, Ajit Desai, Han Du, Rodney J. Garratt, Francisco Rivadeneyra
We demonstrate the ability of reinforcement learning techniques to estimate the best-response functions of banks participating in high-value payments systems—a real-world strategic game of incomplete information.
Content Type(s): Staff research, Staff working papers Topic(s): Digital currencies and fintech, Financial institutions, Financial system regulation and policies, Payment clearing and settlement systems JEL Code(s): A, A1, A12, C, C7, D, D8, D83, E, E4, E42, E5, E58

Using Payments Data to Nowcast Macroeconomic Variables During the Onset of COVID-19

Staff Working Paper 2021-2 James Chapman, Ajit Desai
We use retail payment data in conjunction with machine learning techniques to predict the effects of COVID-19 on the Canadian economy in near-real time. Our model yields a significant increase in macroeconomic prediction accuracy over a linear benchmark model.
Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods, Payment clearing and settlement systems JEL Code(s): C, C5, C53, C55, E, E3, E37, E4, E42, E5, E52

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