Detecting human influence on climate using neural networks based Granger causality

被引：0
作者：
A. Attanasio
U. Triacca
机构：
[1] Università di L’Aquila,
来源：
Theoretical and Applied Climatology | 2011年 / 103卷
关键词：
Hide Layer; Mean Square Error; Neural Network Model; Granger Causality; Global Temperature;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this note we observe that a problem of linear approach to Granger causality testing between CO2 and global temperature is that such tests can have low power. The probability to reject the null hypothesis of non-causality when it is false is low. Regarding non-linear Granger causality, based on multi-layer feed-forward neural network, the analysis provides evidence of significant unidirectional Granger causality from CO2 to global temperature.
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页码：103 / 107
页数：4
相关论文
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