Learning Dynamic Causal Relationships Among Sugar Prices

作     者：Jing XU Xing-wei TONG Fang WANG Jian-ping CHEN 

作者机构：School of StatisticsUniversity of International Business and EconomicsBeijing 100029China School of StatisticsBeijing Normal UniversityBeijing 100875China COFCO Tunhe Co.LtdUrumqiXinjiang 830000China 

出 版 物：《Acta Mathematicae Applicatae Sinica》 (应用数学学报（英文版）)

年 卷 期：2017年第33卷第3期

页      面：809-818页

核心收录：

学科分类：12[管理学] 02[经济学] 07[理学] 070103[理学-概率论与数理统计] 120301[管理学-农业经济管理] 120202[管理学-企业管理（含：财务管理、市场营销、人力资源管理）] 0202[经济学-应用经济学] 020208[经济学-统计学] 1203[管理学-农林经济管理] 1202[管理学-工商管理] 020205[经济学-产业经济学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 

基　　金：Supported by National Natural Science Foundation of China under grant(No.11371062,11671338) Beijing Center for Mathematics and Information Interdisciplinary Science and UIBE Networking Collaboration Center for China’s Multinational Business(No.201504YY006A) 

主　　题：Bayesian Network dynamic causal relationships Bayesian model selection Bayesian model averaging 

摘      要：In this paper, we are interested in exploring the dynamic causal relationships among two sets of three variables in different quarters. One set is futures sugar closing price in Zhengzhou futures exchange market （ZC）, spot sugar price in Zhengzhou （ZS） and futures sugar closing price in New York futures exchange market（NC） and the other includes futures sugar opening price in Zhengzhou （ZO）, ZS and NC. For each quarter, we first use Bayesian model selection to obtain the optimal causal graph with the highest BD scores and then use Bayesian model averaging approach to explore the causal relationship between every two variables. From the real data analysis, the two conclusions almost coincide, which shows that the two methods are practical.