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Forecasting World Trade Using Big Data and Machine Learning Techniques

Author

Listed:
  • Andrei Dubovik
  • Adam Elbourne

    (CPB Netherlands Bureau for Economic Policy Analysis)

  • Bram Hendriks

    (CPB Netherlands Bureau for Economic Policy Analysis)

  • Mark Kattenberg

    (CPB Netherlands Bureau for Economic Policy Analysis)

Abstract

We compare machine learning techniques to a large Bayesian VAR for nowcasting and forecasting world merchandise trade. We focus on how the predictive performance of the machine learning models changes when they have access to a big dataset with 11,017 data series on key economic indicators. The machine learning techniques used include lasso, random forest and linear ensembles. We additionally compare the accuracy of the forecasts during and outside the Great Financial Crisis. We find no statistically significant differences in forecasting accuracy whether with respect to the technique, the dataset used - small or big - or the time period.

Suggested Citation

  • Andrei Dubovik & Adam Elbourne & Bram Hendriks & Mark Kattenberg, 2022. "Forecasting World Trade Using Big Data and Machine Learning Techniques," CPB Discussion Paper 441, CPB Netherlands Bureau for Economic Policy Analysis.
    • Handle: RePEc:cpb:discus:441
    DOI: 10.34932/01mq-sn15
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    References listed on IDEAS

    as
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    More about this item

    JEL classification:

    • F17 - International Economics - - Trade - - - Trade Forecasting and Simulation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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