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China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach
Author(s) -
Shao Yongtong,
Xiong Tao,
Li Minghao,
Hayes Dermot,
Zhang Wendong,
Xie Wei
Publication year - 2021
Publication title -
american journal of agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.949
H-Index - 111
eISSN - 1467-8276
pISSN - 0002-9092
DOI - 10.1111/ajae.12137
Subject(s) - support vector machine , random forest , sample (material) , regression , china , econometrics , consumption (sociology) , regression analysis , linear regression , yield (engineering) , statistics , computer science , artificial neural network , agriculture , sample size determination , artificial intelligence , machine learning , economics , mathematics , geography , chemistry , materials science , social science , archaeology , chromatography , sociology , metallurgy
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 outperforms 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.