Seemingly unrelated regression based copula: An application on thai rice market

• Main Authors: Pathairat Pastpipatkul, Paravee Maneejuk, Aree Wiboonpongse, Songsak Sriboonchitta
• Format: Book Series
• Published: 2018
• Subjects: Computer Science
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84952700781&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/55592
• Institution: Chiang Mai University

© Springer International Publishing Switzerland 2016. This paper introduced the seemingly unrelated regression (SUR) model based on Copula to improve a linear regression system since the conventional SUR model has a strong assumption of normally distributed residuals. The Copula density functions were incorporated into the likelihood to relax the restriction of the marginal distribution. The real dataset of Thai rice was used for an application comparing the conventional SUR model estimated by GLS and the Copula-based SUR model. The result indicated that the Copula-based SUR model performed slightly better than the conventional SUR. In addition, the estimated results showed that Gaussian Copula was the most appropriate function for being the linkage between the marginal distributions. Moreover, the marginal distributions also were tested, and the result showed that a normal distribution and student-t distribution were the best fit for the marginal distributions of demand and supply equations, respectively.