Can Machine Learning Catch the COVID-19 Recession?

Philippe Goulet Coulombe, Massimiliano Marcellino and Dalibor Stevanovic
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Philippe Goulet Coulombe: University of Pennsylvania

No 21-01, Working Papers from Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management

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. (search for similar items in EconPapers)
JEL-codes: C53 C55 E37 (search for similar items in EconPapers)
Pages: 41 pages
Date: 2021-03
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Published, National Institute Economic Review

Downloads: (external link)
https://chairemacro.esg.uqam.ca/wp-content/uploads/sites/146/GCMS_UK_COVID.pdf Revised version, 2020 (application/pdf)

Related works:
Journal Article: CAN MACHINE LEARNING CATCH THE COVID-19 RECESSION? (2021)
Working Paper: Can Machine Learning Catch the COVID-19 Recession? (2021)
Working Paper: Can Machine Learning Catch the COVID-19 Recession? (2021)
Working Paper: Can Machine Learning Catch the COVID-19 Recession? (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:bbh:wpaper:21-01

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