Covid-19 outbreak and beyond: The information content of registered short-time workers for GDP now- and forecasting - Sylvia Kaufmann (Swiss National Bank, Study Center Gerzensee, 2020)
The number of employees historically filed and registered from January to April 2020 for short-time compensation is used to obtain a nowcast for GDP growth in the first quarter and an outlook until the third quarter 2021. We purge the monthly log level series from the systematic component to extract unexpected changes or shocks to log short-time workers. These monthly shocks are included in a univariate model for quarterly GDP growth to capture timely, current-quarter unexpected changes in growth dynamics. Included shocks explain additionally 24% in GDP growth variation. The model is able to forecast quite precisely the decrease in GDP during the financial crisis. It predicts a mean decline in GDP of 5.7% over the next two quarters. Without additional growth stimulus, the GDP level forecast remains persistently 4% lower in the long run. The uncertainty is large, as the 95% highest forecast density interval covers a decrease in GDP as large as 9%. A recovery top re-crisis GDP level in 2021 lies only in the upper tail of the 95% highest forecast density interval.
Keywords: E23; E27; C32; C53; 330; Bayesian analysis; Covid-19; two-step regression; forecasting