Macroeconomic Predictions Using Payments Data and Machine Learning

James Chapman and Ajit Desai

Staff Working Papers from Bank of Canada

Abstract: Predicting the economy’s short-term dynamics—a vital input to economic agents’ decision-making 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.

Keywords: Business fluctuations and cycles; Econometric and statistical methods; Payment clearing and settlement systems (search for similar items in EconPapers)
JEL-codes: C53 C55 E37 E42 E52 (search for similar items in EconPapers)
Pages: 46 pages
Date: 2022-03
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa, nep-fdg, nep-mac and nep-pay
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.bankofcanada.ca/wp-content/uploads/2022/03/swp2022-10.pdf Full text (application/pdf)

Related works:
Journal Article: Macroeconomic Predictions Using Payments Data and Machine Learning (2023)
Working Paper: Macroeconomic Predictions using Payments Data and Machine Learning (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bca:bocawp:22-10

Access Statistics for this paper

More papers in Staff Working Papers from Bank of Canada 234 Wellington Street, Ottawa, Ontario, K1A 0G9, Canada. Contact information at EDIRC.
Bibliographic data for series maintained by ().