Forecasting in the absence of precedent

ABSTRACT

We survey approaches to macroeconomic forecasting during the COVID-19 pandemic. Due to the unprecedented nature of the episode, there was greater dependence on information outside the econometric model, captured through either adjustments to the model or additional data. The transparency and flexibility of assumptions were especially important for interpreting real-time forecasts and updating forecasts as new data were observed. We revisit these themes with a time-varying parameter (TVP) vector autoregression (VAR), which attributes the large jumps primarily to increased volatility rather than changes in the type or propagation of shocks.