A non-linear Granger-causality framework to investigate climate-vegetation dynamics

Satellite Earth observation has led to the creation of global climate data records of many important environmental and climatic variables. These come in the form of multivariate time series with different spatial and temporal resolutions. Data of this kind provide new means to further unravel the influence of climate on vegetation dynamics. However, as advocated in this article, commonly used statistical methods are often too simplistic to represent complex climate-vegetation relationships due to linearity assumptions. Therefore, as an extension of linear Granger-causality analysis, we present a novel non-linear framework consisting of several components, such as data collection from various databases, time series decomposition techniques, feature construction methods, and predictive modelling by means of random forests. Experimental results on global data sets indicate that, with this framework, it is possible to detect non-linear patterns that are much less visible with traditional Granger-causality methods. In addition, we discuss extensive experimental results that highlight the importance of considering non-linear aspects of climate-vegetation dynamics

HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity

The analysis of the interdependence between time series has become an important field of research in the last years, mainly as a result of advances in the characterization of dynamical systems from the signals they produce, the introduction of concepts such as generalized and phase synchronization and the application of information theory to time series analysis. In neurophysiology, different analytical tools stemming from these concepts have added to the ‘traditional’ set of linear methods, which includes the cross-correlation and the coherency function in the time and frequency domain, respectively, or more elaborated tools such as Granger Causality. This increase in the number of approaches to tackle the existence of functional (FC) or effective connectivity (EC) between two (or among many) neural networks, along with the mathematical complexity of the corresponding time series analysis tools, makes it desirable to arrange them into a unified-easy-to-use software package. The goal is to allow neuroscientists, neurophysiologists and researchers from related fields to easily access and make use of these analysis methods from a single integrated toolbox. Here we present HERMES (http://hermes.ctb.upm.es), a toolbox for the Matlab® environment (The Mathworks, Inc), which is designed to study functional and effective brain connectivity from neurophysiological data such as multivariate EEG and/or MEG records. It includes also visualization tools and statistical methods to address the problem of multiple comparisons. We believe that this toolbox will be very helpful to all the researchers working in the emerging field of brain connectivity analysis

Causes and hazards of the euro area sovereign debt crisis: Pure and fundamentals-based contagion

This paper tries to contribute to the understanding of sovereign debt crises' pattern by empirically investigating the determinants of the recent euro area crisis to assess if its transmission was due to "pure" or "fundamentals-based" contagion. Using sovereign bond yield spreads with respect to Germany for a sample of ten central and peripheral countries from January 1999 to December 2012, we firstly examine the dynamic evolution of Granger-causality within the 90 pairs of yield spreads in our sample to detect episodes of contagion (associated with episodes of significant intensification in causality). Secondly, we make use of a logit model to explore whether there is evidence of "pure contagion" or "fundamentals-based contagion", by trying to determine which factors might have been behind the detected contagion episodes. Our results suggest that contagion episodes are concentrated just after the inception of the EMU and matching the Global Financial Crisis, yielding more accurate and sensible indicators than those obtained from DCC-GARCH models used in prior studies. Indeed, they preceded the outburst of the Global Financial Crisis (causality intensification is detected from March 2008), and reached a peak during January-May 2011. Furthermore, they underline the coexistence of "pure" and "fundamentals-based contagion" during the recent European debt crisis