Working Paper: CEPR ID: DP15245
Authors: Michele Lenza; Giorgio Primiceri
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.
Keywords: COVID-19; Volatility; Outliers; Density Forecasts
JEL Codes: C32; E32; E37; C11
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Dropping extreme observations (C46) | Parameter estimation not significantly distorted (C51) |
| Dropping extreme observations (C46) | Vast underestimation of uncertainty in forecasts (C53) |
| Modeling change in shock volatility (C58) | Improved predictive capabilities (C53) |