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

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 mode...

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Autor principal: Kaufmann, Sylvia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7484615/
https://www.ncbi.nlm.nih.gov/pubmed/32934939
http://dx.doi.org/10.1186/s41937-020-00053-x
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author Kaufmann, Sylvia
author_facet Kaufmann, Sylvia
author_sort Kaufmann, Sylvia
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description 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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spelling pubmed-74846152020-09-11 COVID-19 outbreak and beyond: the information content of registered short-time workers for GDP now- and forecasting Kaufmann, Sylvia Swiss J Econ Stat Original Article 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. Springer International Publishing 2020-09-11 2020 /pmc/articles/PMC7484615/ /pubmed/32934939 http://dx.doi.org/10.1186/s41937-020-00053-x Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Original Article
Kaufmann, Sylvia
COVID-19 outbreak and beyond: the information content of registered short-time workers for GDP now- and forecasting
title COVID-19 outbreak and beyond: the information content of registered short-time workers for GDP now- and forecasting
title_full COVID-19 outbreak and beyond: the information content of registered short-time workers for GDP now- and forecasting
title_fullStr COVID-19 outbreak and beyond: the information content of registered short-time workers for GDP now- and forecasting
title_full_unstemmed COVID-19 outbreak and beyond: the information content of registered short-time workers for GDP now- and forecasting
title_short COVID-19 outbreak and beyond: the information content of registered short-time workers for GDP now- and forecasting
title_sort covid-19 outbreak and beyond: the information content of registered short-time workers for gdp now- and forecasting
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7484615/
https://www.ncbi.nlm.nih.gov/pubmed/32934939
http://dx.doi.org/10.1186/s41937-020-00053-x
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