Multivariate Out-of-Sample Tests for Granger Causality
Multivariate Out-of-Sample Tests for Granger Causality
复制标题
格兰杰因果关系的多变量样本外检验
DOI：
10.2139/ssrn.905213
复制
发表时间：
2006
期刊：
影响因子：
--
通讯作者：
C. Croux
中科院分区：
文献类型：
--
作者：
S. Gelper;C. Croux
如果一个时间序列在预测时具有增量预测能力，则称其格兰杰导致另一个序列。虽然格兰杰因果检验已在单变量设置进行了广泛的研究，但对多变量情况知之甚少。提出了格兰杰因果关系的多变量样本外检验，并通过模拟研究对其性能进行了测量。结果用大小-功率图图形化地表示。由此可见，多元回归检验在考虑的可能性中是最有效的。作为实际数据应用，研究了德国、法国、荷兰和比利时的消费者信心指数是否格兰杰影响零售销售。
A time series is said to Granger cause another series if it has incremental predictive power when forecasting it. While Granger causality tests have been studied extensively in the univariate setting, much less is known for the multivariate case. Multivariate out-of-sample tests for Granger causality are proposed and their performance is measured by a simulation study. The results are graphically represented by size-power plots. It emerges that the multivariate regression test is the most powerful among the considered possibilities. As a real data application, it is investigated whether the consumer confidence index Granger causes retail sales in Germany, France, the Netherlands and Belgium.