Can Machine Learning Catch the COVID-19 Recession?

TL;DR

This paper investigates the effectiveness of machine learning methods in forecasting the UK economy during the COVID-19 recession, emphasizing the importance of nonlinearity and extrapolation capabilities.

Contribution

It provides an extensive evaluation of various ML methods for macroeconomic forecasting during unprecedented times, highlighting the importance of nonlinearity and extrapolation.

Findings

ML methods with nonlinear capabilities improve forecast accuracy.

Extrapolative ML models outperform non-extrapolative ones during the pandemic.

Linear components enhance the forecasting ability of some ML models.

Abstract

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.