Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/237697 
Erscheinungsjahr: 
2021
Schriftenreihe/Nr.: 
ECB Working Paper No. 2558
Verlag: 
European Central Bank (ECB), Frankfurt a. M.
Zusammenfassung: 
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.
Schlagwörter: 
COVID-19
Forecasting
Student's t errors
tilting
ination
VAR
JEL: 
C53
E31
E37
Persistent Identifier der Erstveröffentlichung: 
ISBN: 
978-92-899-4558-5
Dokumentart: 
Working Paper

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