China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach

Campus Units

Economics, Finance, Center for Agricultural and Rural Development

Document Type


Publication Version

Submitted Manuscript

Publication Date


Journal or Book Title

American Journal of Agricultural Economics




Small sample size often limits forecasting tasks such as the prediction of production, yield, and consumption of agricultural products. Machine learning offers an appealing alternative to traditional forecasting methods. In particular, Support Vector Regression has superior forecasting performance in small sample applications. In this article, we introduce Support Vector Regression via an application to China’s hog market. Since 2014, China’s hog inventory data has experienced an abnormal decline that contradicts price and consumption trends. We use Support Vector Regression to predict the true inventory based on the price-inventory relationship before 2014. We show that, in this application with a small sample size, Support Vector Regression out-performs neural networks, random forest, and linear regression. Predicted hog inventory decreased by 3.9% from November 2013 to September 2017, instead of the 25.4% decrease in the reported data.

JEL Classification

Q02, Q13, Q17


This is a working paper of an article published as Shao, Yongtong, Tao Xiong, Minghao Li, Dermot Hayes, Wendong Zhang, and Wei Xie. "China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach." American Journal of Agricultural Economics (2020). doi: 10.1111/ajae.12137. Posted with permission.

Copyright Owner

Agricultural & Applied Economics Association



File Format


Published Version