The COVID-19 pandemic and the resulting public health mitigation have caused large-scale economic disruptions globally. During this time, there is an increased need to predict the macroeconomy's short-term dynamics to ensure the effective implementation of fiscal and monetary policy. However, economic prediction during a crisis is challenging because of the unprecedented economic impact, which increases the unreliability of traditionally used linear models that use lagged data. We help address these challenges by using timely retail payments system data in linear and nonlinear machine learning models. We find that compared to a benchmark, our model has a roughly 15 to 45% reduction in Root Mean Square Error when used for macroeconomic nowcasting during the global financial crisis. For nowcasting during the COVID-19 shock, our model predictions are much closer to the official estimates.

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