COVID-19 outbreak and beyond: The information content of registered short-time workers for GDP now- and forecasting
Year of publication: |
2020
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Authors: | Kaufmann, Sylvia |
Published in: |
Swiss Journal of Economics and Statistics. - Heidelberg : Springer, ISSN 2235-6282. - Vol. 156.2020, 1, p. 1-12
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Publisher: |
Heidelberg : Springer |
Subject: | Bayesian analysis | COVID-19 | Two-step regression | Forecasting |
Type of publication: | Article |
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Type of publication (narrower categories): | Article |
Language: | English |
Other identifiers: | 10.1186/s41937-020-00053-x [DOI] 1744180792 [GVK] hdl:10419/259754 [Handle] |
Classification: | E23 - Production ; E27 - Forecasting and Simulation ; C32 - Time-Series Models ; C53 - Forecasting and Other Model Applications |
Source: |
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Kaufmann, Sylvia, (2020)
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Kaufmann, Sylvia, (2020)
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Kaufmann, Sylvia, (2020)
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