The COVID-19 Shock: A Bayesian Approach

Abstract

The coronavirus crisis that started in December 2019 was declared a pandemic by March 2020 and had devastating global consequences. The spread of the virus led to the implementation of different preventive measures prior to the availability of effective vaccines. While many governments implemented lockdowns to counter the pandemic, others did not let the virus halt economic activity. In this paper, we use a Bayesian Vector Autoregressive framework to study the effects of the pandemic on prices, unemployment rates, and interest rates in nine countries that took distinctive approaches in tackling the pandemic, where we introduce lockdowns as shocks to unemployment. Based on impulse response functions, we find that in most countries the unemployment rate rose, interest rates fell or turned negative, and prices fell initially following the implementation of the lockdown measures. However, the massive fiscal and monetary stimulus packages to counteract the effects of the pandemic reversed some of the effects on the variables, suggesting that models with explicit recognition of such effects should be developed.

Testing oil price volatility during Covid-19: Global economic impact

“…In turn, this procedure cuts the uncertainty in crude costs and triggers the unpredictability in crude costs at very minimal stages in the long term. Generally, the results back the proofs given by ( Younes and Altug, 2021 ), who illustrated that the economic downturn plus financial crisis cause more significant crude costs uncertainty. Concerning the overall views, we proffer that achieved crude cost unpredictability advancements result from crude price dynamics variations of crude consumption and production beyond.…”