Detecting human influence on climate using neural networks based Granger causality

Attanasio, A.; Triacca, U.
January 2011
Theoretical & Applied Climatology;Jan2011, Vol. 103 Issue 1-2, p103
Academic Journal
In this note we observe that a problem of linear approach to Granger causality testing between CO 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 CO to global temperature.


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