 Backcasting world trade growth using data reduction methodsAmélie Charles and Olivier Darné The World Economy, 2022, vol. 45, issue 10, 3169-3191 Abstract: This paper compares backcasting performance of models based on variable selection to dynamic factor model for backcasting the world trade growth rate with two months ahead. The variable selection models are specified by applying penalised regressions and an automatic general‐to‐specific procedure, using a large data set. A recursive forecast study is carried out to assess the backcasting performance by distinguishing crisis and non‐crisis periods. The results show that, some selection‐based models exhibit a good backcasting performance during both periods. The more accurate backcasts seem to be SCAD, adaptive Elastic‐Net and adaptive SCAD during the global financial crisis (GFC) and COVID‐19 crisis, whereas it seems rather Lasso, Elastic‐Net, adaptive Lasso and DFM during the non‐crisis period. Amongst the predictors for backcasting world trade growth, it appears that the index of global economic conditions proposed by Baumeister et al. (The Review of Economics and Statistics, 2020), the PMI indicator on new export orders in manufacturing sector and the MSCI world index are relevant. Date: 2022 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:worlde:v:45:y:2022:i:10:p:3169-3191 Ordering information: This journal article can be ordered from Access Statistics for this article The World Economy is currently edited by David Greenaway More articles in The World Economy from Wiley Blackwell |
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