The response of household debt to COVID-19 using a neural networks VAR in OECD.

This paper investigates responses of household debt to COVID-19-related data like confirmed cases and confirmed deaths within a neural networks panel VAR for OECD countries. Our model also includes a plethora of non-pharmaceutical and pharmaceutical interventions. We opt for a global neural networks panel VAR (GVAR) methodology that nests all OECD countries in the sample. Because linear factor models are unable to capture the variability in our data set, the use of an artificial neural network (ANN) method permits to capture this variability. The number of factors, as well as the number of intermediate layers, is determined using the marginal likelihood criterion and we estimate the GVAR with MCMC techniques. We also report δ-values that capture the dominance of each individual country in the network. In terms of dominant countries, the UK, the USA, and Japan dominate interconnections within the network, but also countries like Belgium, Netherlands, and Brazil. Results reveal that household debt positively responds to COVID-19 infections and deaths. Lockdown measures such as stay-at-home advice, and closing schools, all have a positive impact on household debt, though they are of transitory nature. However, vaccinations and testing appear to negatively affect household debt.