Getting back on track: Forecasting after extreme observations

This paper examines the forecast accuracy of cointegrated vector autoregressive models when confronted with extreme observations at the end of the sample period. We focus on comparing two outlier correction methods—additive outlier corrections and innovational outlier corrections—within a forecasting framework for macroeconomic variables. Drawing on data from the COVID-19 pandemic, we empirically demonstrate that cointegrated vector autoregressive models incorporating additive outlier corrections outperform both those with innovational outlier corrections and no outlier corrections in forecasting post-pandemic household consumption. Theoretical analysis and Monte Carlo simulations further support these findings, demonstrating that additive outlier adjustments are particularly effective when macroeconomic variables rapidly return to their initial trajectories following short-lived extreme observations, as is often the case with pandemics. These results carry significant implications for macroeconomic forecasting, emphasising the usefulness of additive outlier corrections in enhancing forecasts after periods of transient extreme observations.