China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach,American Journal of Agricultural Economics

当前位置： X-MOL 学术 › Am. J. Agric. Econ. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

China's Missing Pigs: Correcting China's Hog Inventory Data Using a Machine Learning Approach
American Journal of Agricultural Economics ( IF 4.2 ) Pub Date : 2020-09-18 , DOI: 10.1111/ajae.12137
Yongtong Shao , Tao Xiong , Minghao Li , Dermot Hayes , Wendong Zhang , Wei Xie

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.

更新日期：2020-09-18

点击分享查看原文

点击收藏

公开下载

阅读更多本刊最新论文本刊介绍/投稿指南11