Abstract:
The coronavirus, that started in December 2019, became a pandemic that hit the world
economy and had devastating consequences. The spread of the virus suggested
preventive measures knowing that no vaccine was available. Therefore, city, district and
then country-wide lockdowns were implemented. These variations are introduced as
shocks to unemployment, and will be studied in a Vector Autoregressive Framework.
The shock to unemployment will be discussed using Bayesian inference. This approach
has well-known advantages when studying heavily parameterized models like VARs, as
it assigns prior probabilities to the model parameters. These prior beliefs can be updated
whenever new information is available. This in turn can help in modelling and
forecasting changes that occur following the shock.