Detecting human influence on climate using neural networks based Granger causality |
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Authors: | A Attanasio U Triacca |
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Institution: | 1. Universit?? di L??Aquila, L??Aquila, Italy
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Abstract: | 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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