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Macroeconomic predictions using payments data and machine learning

Bibliographic Details
Authors and Corporations: Chapman, James (Author), Desai, Ajit (Author)
Title: Macroeconomic predictions using payments data and machine learning/ by James T.E. Chapman and Ajit Desai
Language: English
published:
[Ottawa] Bank of Canada [2022]
Series: Bank of Canada: Staff working paper ; 2022, 10
Item Description: 1 Online-Ressource (circa 46 Seiten); Illustrationen
DOI: 10.34989/swp-2022-10
Description
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.