Can Machine Learning Catch the COVID-19 Recession?
This work addresses forecasting challenges for economists and policymakers during unprecedented economic disruptions, though it is incremental as it builds on existing ML approaches.
The study tackled forecasting during the COVID-19 recession by using machine learning methods on a new UK macroeconomic dataset, finding that methods allowing for nonlinearity and extrapolation, such as those with linear dynamic components, performed best in pseudo-out-of-sample exercises.
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.