Backcasting world trade growth using data reduction methods

Amélie Charles and Olivier Darné (olivier.darne@univ-nantes.fr)
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Amélie Charles: USN AM - Université Sorbonne Nouvelle - Paris 3 - UFR Arts et médias - Université Sorbonne Nouvelle - Paris 3, Audencia Business School
Olivier Darné: LEMNA - Laboratoire d'économie et de management de Nantes Atlantique - Nantes Univ - IAE Nantes - Nantes Université - Institut d'Administration des Entreprises - Nantes - Nantes Université - pôle Sociétés - Nantes Univ - Nantes Université

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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 noncrisis 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.

Keywords: backcasting; general- to-specific approach; shrinkage methods; variable selection; world trade growth (search for similar items in EconPapers)
Date: 2022
New Economics Papers: this item is included in nep-int
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Published in The World Economy, 2022, 45 (10), pp.3169 - 3191. ⟨10.1111/twec.13274⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04027843

DOI: 10.1111/twec.13274

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