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The COVID-19 shock and challenges for time series models

Elena Bobeica () and Benny Hartwig

No 2558, Working Paper Series from European Central Bank

Abstract: 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)
Date: 2021-05
New Economics Papers: this item is included in nep-ecm, nep-eec, nep-ets and nep-mac
Note: 2382002
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