Machine Learning in Gravity Models: An Application to Agricultural Trade

Munisamy Gopinath, Feras A. Batarseh and Jayson Beckman

No 27151, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Predicting agricultural trade patterns is critical to decision making in the public and private domains, especially in the current context of trade disputes among major economies. Focusing on seven major agricultural commodities with a long history of trade, this study employed data-driven and deep-learning processes: supervised and unsupervised machine learning (ML) techniques – to decipher patterns of trade. The supervised (unsupervised) ML techniques were trained on data until 2010 (2014), and projections were made for 2011-2016 (2014-2020). Results show the high relevance of ML models to predicting trade patterns in near- and long-term relative to traditional approaches, which are often subjective assessments or time-series projections. While supervised ML techniques quantified key economic factors underlying agricultural trade flows, unsupervised approaches provide better fits over the long-term.

JEL-codes: C45 F14 Q17 (search for similar items in EconPapers)
Date: 2020-05
New Economics Papers: this item is included in nep-agr, nep-big, nep-cmp and nep-int
Note: ITI
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Citations: View citations in EconPapers (8)

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