Machine Learning in Gravity Models: An Application to Agricultural Trade

37 Pages Posted: 18 May 2020 Last revised: 10 Mar 2021

See all articles by Munisamy Gopinath

Munisamy Gopinath

Oregon State University - Department of Applied Economics

Feras Batarseh

George Mason University

Jayson Beckman

U.S. Department of Agriculture (USDA) - Economic Research Service (ERS)

Date Written: May 2020

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.

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Suggested Citation

Gopinath, Munisamy and Batarseh, Feras and Beckman, Jayson, Machine Learning in Gravity Models: An Application to Agricultural Trade (May 2020). NBER Working Paper No. w27151, Available at SSRN: https://ssrn.com/abstract=3603781

Munisamy Gopinath (Contact Author)

Oregon State University - Department of Applied Economics ( email )

213 Ballard Extension Hall
Corvallis, OR 97331-4501
United States
541-737-1402 (Phone)
541-737-2563 (Fax)

Feras Batarseh

George Mason University ( email )

4400 University Drive
Fairfax, VA
United States

HOME PAGE: http://https://science.gmu.edu/directory/feras-batarseh

Jayson Beckman

U.S. Department of Agriculture (USDA) - Economic Research Service (ERS) ( email )

355 E Street, SW
Washington, DC 20024-3221
United States

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