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Backcasting world trade growth using data reduction methods

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  • Amélie Charles
  • Olivier Darné

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.

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  • Amélie Charles & Olivier Darné, 2022. "Backcasting world trade growth using data reduction methods," The World Economy, Wiley Blackwell, vol. 45(10), pages 3169-3191, October.
    • Handle: RePEc:bla:worlde:v:45:y:2022:i:10:p:3169-3191
    DOI: 10.1111/twec.13274
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    Cited by:

    1. Menzie Chinn & Baptiste Meunier & Sebastian Stumpner, 2023. "Nowcasting world trade in real time with machine learning [Estimation du commerce mondial en temps réel grâce à l’apprentissage automatique]," Bulletin de la Banque de France, Banque de France, issue 248.

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