Abstract
This paper investigates whether a heavy-tailed or time-varying volatility error structure is better suited in dealing with the abnormal COVID-19 observations in a Bayesian VAR and discusses pitfalls of using mechanical prior updates. This paper presents evidence that the COVID-19 shock is better captured as a rare event rather than a persistent increase in volatility. Not accounting for heavy-tailed errors may lead to imprecise density forecasts during the pandemic. This paper shows that mechanical updates of prior distributions which depend on scale estimates - such as the commonly used Minnesota prior - may be another source of parameter instability. To mitigate this sensitivity, a COVID-19 robust prior calibration strategy is put forward.
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