ICA Based Identification of Time-Varying Linear Causal Model

作     者：Hongxia Chen Jimin Ye 

作者机构：School of Mathematics and Statistics Xidian University 

出 版 物：《Journal of Harbin Institute of Technology(New Series)》 (哈尔滨工业大学学报（英文版）)

年 卷 期：2019年第26卷第4期

页      面：32-40页

学科分类：02[经济学] 07[理学] 08[工学] 070103[理学-概率论与数理统计] 0810[工学-信息与通信工程] 0202[经济学-应用经济学] 020208[经济学-统计学] 080203[工学-机械设计及理论] 0805[工学-材料科学与工程（可授工学、理学学位）] 0714[理学-统计学（可授理学、经济学学位）] 0802[工学-机械工程] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Sponsored by the National Natural Science Foundation of China(Grant No.61573014) 

主　　题：time-varying causal model independent component analysis(ICA) granger causality test causality inference 

摘      要：Recently, several approaches have been proposed to discover the causality of the time-independent or fixed causal model. However, in many realistic applications, especially in economics and neuroscience, causality among variables might be time-varying. A time-varying linear causal model with non-Gaussian noise is considered and the estimation of the causal model from observational data is focused. Firstly, an independent component analysis(ICA) based two stage method is proposed to estimate the time-varying causal coefficients. It shows that, under appropriate assumptions, the time varying coefficients in the proposed model can be estimated by the proposed approach, and results of experiment on artificial data show the effectiveness of the proposed approach. And then, the granger causality test is used to ascertain the causal direction among the variables. Finally, the new approach is applied to the real stock data to identify the causality among three stock indices and the result is consistent with common sense.