Identification of Structure VAR Models Using Conditional Mutual Information Graphs
结构VAR模型辨识的条件互信息图模型

A class graphical models,called linear conditional mutual information graph,is proposed for identification structural vector autoregression model.The vertex set denotes random variables at different times,and the directed edges denote causal dependence between the variables.The presence of the edges is tested by a statistics based on linear conditional mutual information.The permutation procedure is used to determine the significance of the test statistics.The direction of the relationships of the current variables is determined by a statistical method and lead to directed acyclic graph.The method is demonstrated by simulation time series with different dependence structures and error distribution.