Search results for "Granger causality."

Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators

2021

One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Sq…

Predictability decomposition detects the impairment of brain-heart dynamical networks during sleep disorders and their recovery with treatment

2016

This work introduces a framework to study the network formed by the autonomic component of heart rate variability (cardiac process η ) and the amplitude of the different electroencephalographic waves (brain processes δ , θ , α , σ , β ) during sleep. The framework exploits multivariate linear models to decompose the predictability of any given target process into measures of self-, causal and interaction predictability reflecting respectively the information retained in the process and related to its physiological complexity, the information transferred from the other source processes, and the information modified during the transfer according to redundant or synergistic interaction betwee…

Assessment of Granger causality by nonlinear model identification: application to short-term cardiovascular variability.

2007

A method for assessing Granger causal relationships in bivariate time series, based on nonlinear autoregressive (NAR) and nonlinear autoregressive exogenous (NARX) models is presented. The method evaluates bilateral interactions between two time series by quantifying the predictability improvement (PI) of the output time series when the dynamics associated with the input time series are included, i.e., moving from NAR to NARX prediction. The NARX model identification was performed by the optimal parameter search (OPS) algorithm, and its results were compared to the least-squares method to determine the most appropriate method to be used for experimental data. The statistical significance of…

Credit risk transmission in the European banking sector: the case of the subprime and Eurozone debt crises

2014

El objetivo del presente trabajo es analizar en profundidad la transmisión del riesgo de crédito, aproximado por los CDS spreads, en el sector bancario europeo durante el periodo 2006-2012, intentando dar respuesta a diversas cuestiones: (i) ¿existe evidencia de transmisión del riesgo de crédito entre las entidades financieras europeas de la Eurozona y las que no pertenecen a dicha zona?, (ii) ¿es esta transmisión bidireccional o unidireccional?, (iii) concretamente, ¿qué países han liderado dicha transmisión?, y (iv) ¿cómo se ha visto afectada dicha transmisión con las recientes crisis financieras? Los resultados indican un cambio significativo en la transmisión del riesgo de crédito con e…

Linear and nonlinear parametric model identification to assess granger causality in short-term cardiovascular interactions

2008

We assessed directional relationships between short RR interval and systolic arterial pressure (SAP) variability series according to the concept of Granger causality. Causality was quantified as the predictability improvement (PI) of a time series obtained when samples of the other series were used for prediction, i.e. moving from autoregressive (AR) to AR exogenous (ARX) prediction. AR and ARX predictions were performed both by linear and nonlinear parametric models. The PIs of RR given SAP and of SAP given RR, measuring baroreflex and mechanical couplings, were calculated in 15 healthy subjects in the resting supine and upright tilt positions. Using nonlinear models we found a bilateral i…

Letter by Masè et al Regarding Article, "Granger Causality-Based Analysis for Classification of Fibrillation Mechanisms and Localization of Rotationa…

2020

Neural networks with non-uniform embedding and explicit validation phase to assess Granger causality

2015

A challenging problem when studying a dynamical system is to find the interdependencies among its individual components. Several algorithms have been proposed to detect directed dynamical influences between time series. Two of the most used approaches are a model-free one (transfer entropy) and a model-based one (Granger causality). Several pitfalls are related to the presence or absence of assumptions in modeling the relevant features of the data. We tried to overcome those pitfalls using a neural network approach in which a model is built without any a priori assumptions. In this sense this method can be seen as a bridge between model-free and model-based approaches. The experiments perfo…

Multiscale Granger causality analysis by à trous wavelet transform

2017

Since interactions in neural systems occur across multiple temporal scales, it is likely that information flow will exhibit a multiscale structure, thus requiring a multiscale generalization of classical temporal precedence causality analysis like Granger's approach. However, the computation of multiscale measures of information dynamics is complicated by theoretical and practical issues such as filtering and undersampling: to overcome these problems, we propose a wavelet-based approach for multiscale Granger causality (GC) analysis, which is characterized by the following properties: (i) only the candidate driver variable is wavelet transformed (ii) the decomposition is performed using the…

Is There a Connection between Sovereign CDS Spreads and the Stock Market? Evidence for European and US Returns and Volatilities

2020

This study complements the current literature, providing a thorough investigation of the lead&ndash

Stock earnings and bond yields in the US 1871–2017 : The story of a changing relationship

2021

Abstract Using historical data spanning almost 150 years, we examine whether there is a long-run equilibrium relationship between the stock's earnings and bond yields. The novelty of our econometric methodology consists in using a vector error correction model where we allow multiple structural breaks in the equilibrium relationship. The results of our analysis suggest the existence of an equilibrium relationship over 1871–1932 and 1958–2017. On the two historical segments, our analysis finds that the stock's earnings yield followed the bond yield in both the short run and long run, but not the other way around. Perhaps the most important and surprising finding of our empirical study is tha…