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Working Papers

June 2021, No. 21-10

Forecasting in the Absence of Precedent

Paul Ho

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. With data available at the time of writing, we show how various assumptions were violated and how these systematically biased forecasts.


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