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COVID-19 outbreak and beyond: the information content of registered short-time workers for GDP now- and forecasting

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  • Sylvia Kaufmann

    (Study Center Gerzensee, Foundation of the Swiss National Bank)

Abstract

The number of short-time workers from January to April 2020 is used to now- and forecast quarterly GDP growth. 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 additionally explain 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 includes a decrease in GDP as large as 9%. A recovery to pre-crisis GDP level in 2021 lies only in the upper tail of the 95% highest forecast density interval.

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  • Sylvia Kaufmann, 2020. "COVID-19 outbreak and beyond: the information content of registered short-time workers for GDP now- and forecasting," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 156(1), pages 1-12, December.
    • Handle: RePEc:spr:sjecst:v:156:y:2020:i:1:d:10.1186_s41937-020-00053-x
    DOI: 10.1186/s41937-020-00053-x
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    1. Christina D. Romer & David H. Romer, 1989. "Does Monetary Policy Matter? A New Test in the Spirit of Friedman and Schwartz," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 121-184, National Bureau of Economic Research, Inc.
    2. Beveridge, Stephen & Nelson, Charles R., 1981. "A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the `business cycle'," Journal of Monetary Economics, Elsevier, vol. 7(2), pages 151-174.
    3. Christina D. Romer & David H. Romer, 2004. "A New Measure of Monetary Shocks: Derivation and Implications," American Economic Review, American Economic Association, vol. 94(4), pages 1055-1084, September.
    4. Eichenauer Vera & Sturm Jan-Egbert, 2020. "Die wirtschaftspolitischen Maßnahmen der Schweiz zu Beginn der Covid-19-Pandemie," Perspektiven der Wirtschaftspolitik, De Gruyter, vol. 21(3), pages 290-300, September.
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    Cited by:

    1. Sylvia Kaufmann, 2023. "Covid-19 outbreak and beyond: a retrospect on the information content of short-time workers for GDP now- and forecasting," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-10, December.
    2. Peter Kugler & George Sheldon, 2023. "A monthly leading indicator of Swiss GDP growth based on Okun’s law," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-14, December.
    3. Sylvia Kaufmann, 2022. "Covid-19 outbreak and beyond: A retrospect on the information content of registered short-time workers for GDP now- and forecasting," Working Papers 22.02R, Swiss National Bank, Study Center Gerzensee.
    4. Rong Fu & Luze Xie & Tao Liu & Juan Huang & Binbin Zheng, 2022. "Chinese Economic Growth Projections Based on Mixed Data of Carbon Emissions under the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(24), pages 1-16, December.

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