Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/229075
Authors: 
Lenza, Michele
Primiceri, Giorgio E.
Year of Publication: 
2020
Series/Report no.: 
ECB Working Paper No. 2461
Abstract: 
This paper illustrates how to handle a sequence of extreme observations-such as those recorded during the COVID-19 pandemic-when estimating a Vector Autoregression, which is the most popular time-series model in macroeconomics. Our results show that the ad-hoc strategy of dropping these observations may be acceptable for the purpose of parameter estimation. However, disregarding these recent data is inappropriate for forecasting the future evolution of the economy, because it vastly underestimates uncertainty.
Subjects: 
COVID-19
Volatility
Outliers
Density Forecasts
JEL: 
C32
E32
E37
C11
Persistent Identifier of the first edition: 
ISBN: 
978-92-899-4378-9
Document Type: 
Working Paper

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