Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/278668 
Year of Publication: 
2023
Series/Report no.: 
ECB Working Paper No. 2836
Publisher: 
European Central Bank (ECB), Frankfurt a. M.
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 performances 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.
Subjects: 
Forecasting
big data
large dataset
factor model
pre-selection
JEL: 
C53
C55
E37
Persistent Identifier of the first edition: 
ISBN: 
978-92-899-6121-9
Document Type: 
Working Paper

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