Abstract
The 2018/2019 trade conflict between the United States and China impacted a broad array of agricultural products, including soybeans. Previous trade studies using gravity models fail to account for trends and complex seasonal patterns observed in the data. This study uses a machine learning (ML) approach to estimate losses in soybean export value and volume from the trade war. We find that models using ML techniques outperform traditional models and estimate losses in the value of soybean exports of $10.16 billion/year. The ML models fit the complex export trade data series well, highlighting the importance of utilizing improved modeling approaches.