The impact of Covid-19 shocks in the US real economy and the availability of credit: A VAR model approach

• Autor(a) principal: Sousa, André Redol de
• Data de Publicação: 2022
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10071/26695
• topic: VAR model; COVID-19; Real economic activity; Crédito -- Credit; Fed funds rate; Cholesky decomposition; Modelo VAR; Atividade económica -- Economic activity; Taxa de juros da Reserva Federal; Decomposição de Cholesky

The following thesis presents an empirical analysis of how Covid-19 affected the real economic activity, credit, and the Fed funds rate in the US between March 4th, 2020, and 9th, 2022. Another question of interest is whether the credit helped boost real economic activity in this period. To achieve the objective, VAR models are employed. To estimate the impact of Covid-19 on these variables, we use OIRF’s. The results indicate that, there will be a negative response in the real economic activity index to the new confirmed cases or deaths growth rate Covid-19 one-standard deviation shock, between 0.3% and 0.16%. Also, the total credit growth rate and the Fed Funds rate are not considerably affected, with an effect close to zero. Moreover, in terms of impact duration, there is a negative effect for fifteen weeks on the real economic activity index caused by the Covid-19 deaths growth rate with an average effect of 0.3%. In the models with the discriminated credit, we see which types of credit most contributed to the short-term economic activity: consumer and commercial and industrial loans, which create a positive effect of about 0.2% and 0.15%, on average one month after the initial shock. Regarding the accumulated impact, consumer loans are the only type of credit that seems effective in boosting real economic activity, with the peak occurring two months after the initial shock with an average effect of 0.24%. Finally, we conclude that our VAR models are not suited to predict future variables values.