Central banks conduct research involving in-depth interviews with external parties—but little is known about how this information affects monetary policy. We address this gap by analyzing open-ended interviews with senior central bank economic and policy staff who work closely with policy decision-makers.
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.