The COVID-19 shock and challenges for time series models
Elena Bobeica () and
No 2558, Working Paper Series from European Central Bank
We document the impact of COVID-19 on frequently employed time series models, with a focus on euro area inflation. 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. JEL Classification: C53, E31, E37
Keywords: COVID-19; forecasting; inflation; student's t errors; tilting; VAR (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:20212558
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