Nowcasting World Trade with Machine Learning: a Three-Step Approach / Menzie D. Chinn, Baptiste Meunier, Sebastian Stumpner.

Format:

Book

Author/Creator:

Chinn, Menzie D.

Contributor:

National Bureau of Economic Research.

Meunier, Baptiste.

Stumpner, Sebastian.

Series:

Working Paper Series (National Bureau of Economic Research) no. w31419.

NBER working paper series no. w31419

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2023.

Summary:

We nowcast world trade using machine learning, distinguishing between tree-based methods (random forest, gradient boosting) and their regression-based counterparts (macroeconomic random forest, gradient linear 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.

Notes:

Print version record

June 2023.