%0 e Book %A Chapman, James and Desai, Ajit %I Bank of Canada %D 2022 %C Ottawa %D 2022 %G English %B Staff working paper / Bank of Canada %~ Galerie für Zeitgenössische Kunst Leipzig %T Macroeconomic predictions using payments data and machine learning %U https://doi.org/10.34989/swp-2022-10 %U https://www.bankofcanada.ca/wp-content/uploads/2022/03/swp2022-10.pdf %7 Last updated: March 3, 2022 %X 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. %Z https://katalog.gfzk.de/Record/0-1794871403 %U https://katalog.gfzk.de/Record/0-1794871403