Nowcasting world trade with machine learning: A three-step approach - Menzie David Chinn; Baptiste Meunier; Sebastian Stumpner (European Central Bank (ECB), 2023)
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.
Keywords: C53; C55; E37; 330; Forecasting; big data; large dataset; factor model; pre-selection; Wirtschaftsprognose; Internationale Wirtschaft; Prognoseverfahren; Künstliche Intelligenz; Data Mining; Big Data; Faktorenanalyse; Nowcasting