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Detecting human influence on climate using neural networks based Granger causality.
- Source :
- Theoretical & Applied Climatology; Jan2011, Vol. 103 Issue 1-2, p103-107, 5p, 3 Charts, 2 Graphs
- Publication Year :
- 2011
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0177798X
- Volume :
- 103
- Issue :
- 1-2
- Database :
- Complementary Index
- Journal :
- Theoretical & Applied Climatology
- Publication Type :
- Academic Journal
- Accession number :
- 56790319
- Full Text :
- https://doi.org/10.1007/s00704-010-0285-8