Machine Learning in Gravity Models: An Application to Agricultural Trade

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. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.