Can Machine Learning Catch the COVID-19 Recession?

41 Pages Posted: 3 Mar 2021

See all articles by Philippe Goulet Coulombe

Philippe Goulet Coulombe

Université du Québec à Montréal - Département des Sciences Économiques

Massimiliano Giuseppe Marcellino

Bocconi University - Department of Economics; Centre for Economic Policy Research (CEPR)

Dalibor Stevanovic

Universite du Quebec a Montreal

Multiple version iconThere are 2 versions of this paper

Date Written: March 2, 2021

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.

Keywords: Machine Learning, Big Data, Forecasting, COVID-19

JEL Classification: C53, C55, E37

Suggested Citation

Goulet Coulombe, Philippe and Marcellino, Massimiliano and Stevanovic, Dalibor, Can Machine Learning Catch the COVID-19 Recession? (March 2, 2021). Available at SSRN: https://ssrn.com/abstract=3796421 or http://dx.doi.org/10.2139/ssrn.3796421

Philippe Goulet Coulombe

Université du Québec à Montréal - Département des Sciences Économiques ( email )

PB 8888 Station DownTown
Succursale Centre Ville
Montreal, Quebec H3C3P8
Canada

Massimiliano Marcellino

Bocconi University - Department of Economics ( email )

Via Gobbi 5
Milan, 20136
Italy

Centre for Economic Policy Research (CEPR) ( email )

London
United Kingdom

Dalibor Stevanovic (Contact Author)

Universite du Quebec a Montreal ( email )

Montréal, QC
Canada

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
210
Abstract Views
1,005
Rank
305,974
PlumX Metrics