Rizwan Fazal PhD , M. Ishaq Bhatti , Atiq Ur Rehman
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引用次数: 2
Abstract
This paper proposes modified Peter and Clark (PC) algorithm of graph-theoretic approach to study causality correlated data. The proposed algorithm is derived to determine the directions of the casual correlated complex variables. The PC algorithm treats VAR residuals as original variables while the proposed algorithm riz-PC uses modified R recursive residuals to find the correct causal direction among policy variables. This study evaluates the performance of these causal search algorithms in term of size and power properties. Our findings suggest that the newly proposed modified riz-PC algorithm can test causality better, as it successfully depicted the correct causal direction and was best at differentiating between true and spurious causality in routine Monte Carlo experiments.
本文提出了改进的图论方法中的Peter and Clark (PC)算法来研究因果关系相关数据。提出了一种确定随机相关复变量方向的算法。PC算法将VAR残差作为原始变量，而riz-PC算法则使用改进的R递归残差来寻找策略变量之间正确的因果方向。本研究评估了这些因果搜索算法在大小和功率特性方面的性能。我们的研究结果表明，新提出的改进的riz-PC算法可以更好地测试因果关系，因为它成功地描述了正确的因果关系方向，并且在常规蒙特卡罗实验中最擅长区分真假因果关系。
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