The COVID-19 shock and challenges for time series models - Elena Bobeica; Benny Hartwig (European Central Bank (ECB), 2021)
We document the impact of COVID-19 on frequently employed time series models, with a focus on euro area in ation. We show that for both single equation models (Phillips curves) and Vector Autoregressions (VARs) estimated parameters change notably with the pandemic. In a VAR, allowing the errors to have a distribution with fatter tails than the Gaussian one equips the model to better deal with the COVID-19 shock. A standard Gaussian VAR can still be used for producing conditional forecasts when relevant off-model information is used. We illustrate this by conditioning on official projections for a set of variables, but also by tilting to expectations from the Survey of Professional Forecasters. For Phillips curves, averaging across many conditional forecasts in a thick modelling framework offers some hedge against parameter instability.
Keywords: C53; E31; E37; 330; COVID-19; Forecasting; Student's t errors; tilting; ination; VAR