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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
Authors
  • Emmanuel Mamatzakis
  • Steven Ongena
  • Mike G Tsionas
Item Subtype Original Work
Refereed Yes
Status Published electronically before print/final form (Epub ahead of print)
Language
  • English
Journal Title Empirical Economics
Geographical Reach international
Volume forthcoming
Page Range -
Date 2022
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.
Official URL https://link.springer.com/article/10.1007/s00181-022-02325-2
Related URLs
Digital Object Identifier 10.1007/s00181-022-02325-2
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