The Nexus between lockdown Shocks and Economic Uncertainty: Empirical Evidence from a VAR model
Lucas Hafemann ()
Additional contact information
Lucas Hafemann: Justus-Liebig-University Giessen
MAGKS Papers on Economics from Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung)
Abstract:
The contribution of this paper is twofold. First, we introduce a daily vector autoregression (VAR) model for the US economy that allows discerning between lockdown shocks and a real business cycle shocks. With this methodology at hand, we then evaluate the impact of lockdown measures on economic uncertainty in a second step. Overall, we only find a moderate positive impact on uncertainty levels that is, in particular, weaker than the impact of the real business cycle shock. Taking a more granular perspective, we observe that in particular uncertainty related to entitlement programs increases and monetary policy uncertainty decreases after a lockdown shock.
Keywords: COVID-19; lockdown; shock identification; market uncertainty (search for similar items in EconPapers)
JEL-codes: E60 E62 E65 G01 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2021
New Economics Papers: this item is included in nep-isf and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations:
Forthcoming in
Downloads: (external link)
https://www.uni-marburg.de/en/fb02/research-groups ... 32-2021_hafemann.pdf First 202132 (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:mar:magkse:202132
Access Statistics for this paper
More papers in MAGKS Papers on Economics from Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung) Contact information at EDIRC.
Bibliographic data for series maintained by Bernd Hayo ().