The COVID-19 shock: A Bayesian approach - Oussama Abi Younes; Sumru Altuğ (Journal of Risk and Financial Management, 2021)
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.
Keywords: 330; coronavirus; pandemic; lockdown; shock; Bayesian VAR; unemployment; stimulus; inflation