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中国工业与应用数学学会会刊
主管：中华人民共和国教育部
主办：西安交通大学
ISSN 1005-3085  CN 61-1269/O1
工程数学学报

工程数学学报 ›› 2016, Vol. 33 ›› Issue (3): 270-278.doi: 10.3969/j.issn.1005-3085.2016.03.005

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基于图模型方法的Granger因果性检验

魏岳嵩   

  1. 淮北师范大学数学科学学院，安徽 淮北 235000
  • 收稿日期:2015-09-14 接受日期:2016-03-21 出版日期:2016-06-15 发布日期:2016-08-15
  • 基金资助:
    国家自然科学基金 (61201323)；安徽省高校自然科学基金 (KJ2015A035).

Granger Causality Detecting Based on Graphical Modelling

WEI Yue-song   

  1. School of Mathematical Science, Huaibei Normal University, Huaibei, Anhui 235000
  • Received:2015-09-14 Accepted:2016-03-21 Online:2016-06-15 Published:2016-08-15
  • Supported by:
    The National Natural Science Foundation of China (61201323); the Natural Science Foundation of Anhui Higher Education Institutions of China (KJ2015A035).

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摘要： Granger因果性是衡量系统变量间动态关系的重要依据．本文利用图模型方法研究变量间的Granger因果性，建立了Granger因果图．基于条件互信息和置换检验法建立了Granger因果图结构的辨识方法，利用混沌理论中的关联积分估计相应的检验统计量，给出了统计量的渐进分布，并用所给方法研究国际主要股市间的Granger因果关系．

关键词: Granger因果图, 条件互信息, 极限分布, 关联积分

Abstract:

The Granger causality is an important criterion for measuring the dynamic relationship among system variables. In this paper, we apply the graphical model method to explore the Granger causal relations among variables. The Granger causality graph is established and its structural identification is investigated based on the conditional mutual information and permutation test. The test statistics is estimated using the correlation integral of chaos theory and its limiting distribution is proved. Finally, the Granger causality among main international stock markets is investigated using the proposed method.

Key words: Granger causality graphs, conditional mutual information, limiting distribution, correlation integral

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