Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/261263 
Year of Publication: 
2022
Series/Report no.: 
Bank of Canada Staff Working Paper No. 2022-10
Publisher: 
Bank of Canada, Ottawa
Abstract: 
Predicting the economy's short-term dynamics-a vital input to economic agents' decisionmaking process-often uses lagged indicators in linear models. This is typically sufficient during normal times but could prove inadequate during crisis periods such as COVID-19. This paper demonstrates: (a) that payments systems data which capture a variety of economic transactions can assist in estimating the state of the economy in real time and (b) that machine learning can provide a set of econometric tools to effectively handle a wide variety in payments data and capture sudden and large effects from a crisis. Further, we mitigate the interpretability and overfitting challenges of machine learning models by using the Shapley value-based approach to quantify the marginal contribution of payments data and by devising a novel cross-validation strategy tailored to macroeconomic prediction models.
Subjects: 
Econometric and statistical methods
Business fluctuations and cycles
Payment clearingand settlement systems
JEL: 
C53
C55
E37
E42
E52
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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