Discussion paper
DP15867 Can Machine Learning Catch the COVID-19 Recession?
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.
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