Forecasting World Trade Using Big Data and Machine Learning Techniques

Andrei Dubovik, Adam Elbourne (), Bram Hendriks () and Mark Kattenberg ()
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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

No 441, CPB Discussion Paper from 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.

JEL-codes: C53 C55 F17 (search for similar items in EconPapers)
Date: 2022-10
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for and nep-int
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Persistent link: https://EconPapers.repec.org/RePEc:cpb:discus:441

DOI: 10.34932/01mq-sn15

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