  Based on evidence gathered from a newly built large macroeconomic data set
for the UK, labeled UK-MD and comparable to similar datasets for the US and
Canada, it seems the most promising avenue for forecasting during the pandemic
is to allow for general forms of nonlinearity by using machine learning (ML)
methods. But not all nonlinear ML methods are alike. For instance, some do not
allow to extrapolate (like regular trees and forests) and some do (when
complemented with linear dynamic components). This and other crucial aspects of
ML-based forecasting in unprecedented times are studied in an extensive
pseudo-out-of-sample exercise.
