  We consider forecast comparison in the presence of instability when this
affects only a short period of time. We demonstrate that global tests do not
perform well in this case, as they were not designed to capture very
short-lived instabilities, and their power vanishes altogether when the
magnitude of the shock is very large. We then discuss and propose approaches
that are more suitable to detect such situations, such as nonparametric methods
(S test or MAX procedure). We illustrate these results in different Monte Carlo
exercises and in evaluating the nowcast of the quarterly US nominal GDP from
the Survey of Professional Forecasters (SPF) against a naive benchmark of no
growth, over the period that includes the GDP instability brought by the
Covid-19 crisis. We recommend that the forecaster should not pool the sample,
but exclude the short periods of high local instability from the evaluation
exercise.
