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Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title The response of household debt to COVID-19 using a neural networks VAR in OECD
Organization Unit
  • Emmanuel C Mamatzakis
  • Steven Ongena
  • Mike G Tsionas
Item Subtype Original Work
Refereed No
Status Published in final form
  • English
Journal Title Empirical Economics
Publisher Springer
Geographical Reach international
ISSN 0377-7332
Volume 65
Page Range 65 - 91
Date 2023
Abstract Text This paper investigates responses of household debt to COVID-19 related data like confirmed cases and confirmed deaths within a panel VAR framework for OECD countries. We also employ a plethora of non-pharmaceutical and pharmaceutical interventions as shocks. In terms of methodology, we opt for a global panel VAR (GVAR) methodology that nests underlying country VARs. In addition, as linear factor models may be unable to capture the variability in the data, we use an artificial neural network (ANN) method. The number of factors, as well as the number of intermediate layers, are determined using the marginal likelihood criterion and we estimate the GVAR with MCMC techniques. Results reveal that household debt positively responds to COVID-19 infections and mortality as well as lockdowns, though this response is valid in the short term. However, vaccinations and testing appear to negatively affect household debt. Lockdown measures such as stay-at-home advice, and closing schools, all have a positive impact on household debt in GVAR, though of transitory nature.
Related URLs
Digital Object Identifier 10.1007/s00181-022-02325-2
Other Identification Number merlin-id:22868
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