Anthropogenic CO2 Emissions and Global Warming: Evidence from Granger Causality Analysis

Bilancia, Massimo; Vitale, Domenico

doi:10.1007/978-3-642-21037-2_21

Cited by 3 publications

(1 citation statement)

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“…Since the existence of correlation between two variables does not necessarily demonstrate the direction of causality, we applied Causality analysis in order to test the significance of causal relationships between specific variables. In this way, we applied the Granger Causality method, which has already been successfully applied to a wide range of data from various fields of social science, economics, engineering and neurosciences, to identify the presence of linear causal interactions between variables, based on prediction theory (Papana et al 2013;Attanasio et al 2013;Pasini et al 2012;Attanasio et al 2012;Bilancia and Vitale 2012;Kodra et al 2011;Seth 2010).…”

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confidence: 99%

Spatiotemporal Analysis of Seawatch Buoy Meteorological Observations

2015

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In the present study, we analyzed meteorological observations from Seawatch buoys in the Mediterranean Sea and specifically from locations in the Aegean and Ionian Sea. The data were collected from buoys that have been deployed by the Hellenic Center for Marine Research (HCMR) in the framework of the POSEIDON project. Our aim was to understand the spatiotemporal underlying characteristics of the meteorological conditions and identify correlation patterns between different locations. For each time series at a given buoy location we estimated mainly non-linear measures such as mutual information combined with simple descriptive statistic measures, as well as some other dynamics detectors such as Hurst Exponent and Hjorth parameters. Furthermore, the relationships between the meteorological variables were investigated using the cross correlation method as well as Granger causality methodology for identification of direction interactions among variables. The results show that the combination of the proposed methods reveals information about the spatiotemporal characteristics of the time series.

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