Nowcasting World Trade with Machine Learning: a Three-Step Approach

Menzie Chinn, Baptiste Meunier and Sebastian Stumpner

Working papers from Banque de France

Abstract: We nowcast world trade using machine learning, distinguishing between tree-based methods (random forest, gradient boosting) and their regression-based counterparts (macroeconomic random forest, linear gradient boosting). While much less used in the literature, the latter are found to outperform not only the tree-based techniques, but also more traditional linear and non-linear techniques (OLS, Markov-switching, quantile regression). They do so significantly and consistently across different horizons and real-time datasets. To further improve performance when forecasting with machine learning, we propose a flexible three-step approach composed of (step 1) pre-selection, (step 2) factor extraction and (step 3) machine learning regression. We find that both pre-selection and factor extraction significantly improve the accuracy of machine-learning-based predictions. This three-step approach also outperforms workhorse benchmarks, such as a PCA-OLS model, an elastic net, or a dynamic factor model. Finally, on top of high accuracy, the approach is flexible and can be extended seamlessly beyond world trade.

Keywords: Forecasting; Big Data; Large Dataset; Factor Model; Pre-Selection (search for similar items in EconPapers)
JEL-codes: C53 C55 E37 (search for similar items in EconPapers)
Pages: 49 pages
Date: 2023
New Economics Papers: this item is included in nep-big and nep-for
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://publications.banque-france.fr/sites/defaul ... /documents/dt917.pdf

Related works:
Working Paper: Nowcasting world trade with machine learning: a three-step approach (2023)
Working Paper: Nowcasting World Trade with Machine Learning: a Three-Step Approach (2023)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bfr:banfra:917

Access Statistics for this paper

More papers in Working papers from Banque de France Banque de France 31 Rue Croix des Petits Champs LABOLOG - 49-1404 75049 PARIS. Contact information at EDIRC.
Bibliographic data for series maintained by Michael brassart ().