How to estimate a VAR after March 2020

Michele Lenza, Giorgio E. Primiceri

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.

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Year of publication:	[2020]
Authors:	Lenza, Michele ; Primiceri, Giorgio E.
Publisher:	Frankfurt am Main, Germany : European Central Bank
Subject:	COVID-19 \| Volatility \| Outliers \| Density Forecasts \| Coronavirus \| Prognoseverfahren \| Forecasting model \| Volatilität \| VAR-Modell \| VAR model \| Zeitreihenanalyse \| Time series analysis \| Statistische Verteilung \| Statistical distribution