Digital currencies store balances in anonymous electronic addresses. This paper analyzes the trade-offs between the safety and convenience of aggregating balances in addresses, electronic wallets and banks.
We use retail payment data in conjunction with machine learning techniques to predict the effects of COVID-19 on the Canadian economy in near-real time. We find this approach yields a significant increase in forecasting precision over a linear benchmark model. This model can help policy-makers before official data are released.