Can Machine Learning Catch the COVID-19 Recession?

Philippe Goulet Coulombe, Massimiliano Marcellino and Dalibor Stevanovic

CIRANO Working Papers from CIRANO

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)
Date: 2021-03-02
New Economics Papers: this item is included in nep-big, nep-cmp and nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

Downloads: (external link)
https://cirano.qc.ca/files/publications/2021s-09.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:cir:cirwor:2021s-09

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