The concept of causality introduced by Wiener (1956) and Granger (1969) is defined in terms of predictability one period ahead. This concept can be generalized by considering causality at a given horizon h, and causality up to any given horizon h [Dufour and Renault (1998)]. This generalization is motivated by the fact that, in the presence of an auxiliary variable vector Z, it is possible that a variable Y does not cause variable X at horizon 1, but causes it at horizon h > 1. In this case, there is an indirect causality transmitted by Z. Another related problem consists in measuring the importance of causality between two variables. Existing causality measures have been defined only for the horizon 1 and fail to capture indirect ca...
Granger-causality in the frequency domain is an emerging tool to analyze the causal relationship be...
Abstract. Granger causality (GC) is one of the most popular measures to re-veal causality influence ...
Abstract Granger causality (GC) has been widely ap-plied in economics and neuroscience to reveal cau...
The concept of causality introduced by Wiener (1956) and Granger (1969) is defined in terms of pred...
The concept of causality introduced by Wiener [Wiener, N., 1956. The theory of prediction, In: E.F. ...
We propose methods for testing hypothesis of non-causality at various horizons, as defined in Dufour...
Abstract: This article investigates causality structure of financial time series. We concentrate on ...
This article investigates the causality structure of financial time series. We concentrate on three ...
Nous proposons des méthodes pour tester des hypothèses de non-causalité à différents horizons, tel q...
A widely agreed upon definition of time series causality inference, established in the sem-inal 1969...
This article investigates the causality structure of financial time series. We concentrate on three ...
We propose a nonparametric estimator and a nonparametric test for Granger causality measures that qu...
We propose a nonparametric estimator and a nonparametric test for Granger causality measures that qu...
In this paper, we develop a parametric test procedure for multiple horizon "Granger" causality and a...
The concepts of weak, strong and strict Granger causality are introduced for nonlinear time series m...
Granger-causality in the frequency domain is an emerging tool to analyze the causal relationship be...
Abstract. Granger causality (GC) is one of the most popular measures to re-veal causality influence ...
Abstract Granger causality (GC) has been widely ap-plied in economics and neuroscience to reveal cau...
The concept of causality introduced by Wiener (1956) and Granger (1969) is defined in terms of pred...
The concept of causality introduced by Wiener [Wiener, N., 1956. The theory of prediction, In: E.F. ...
We propose methods for testing hypothesis of non-causality at various horizons, as defined in Dufour...
Abstract: This article investigates causality structure of financial time series. We concentrate on ...
This article investigates the causality structure of financial time series. We concentrate on three ...
Nous proposons des méthodes pour tester des hypothèses de non-causalité à différents horizons, tel q...
A widely agreed upon definition of time series causality inference, established in the sem-inal 1969...
This article investigates the causality structure of financial time series. We concentrate on three ...
We propose a nonparametric estimator and a nonparametric test for Granger causality measures that qu...
We propose a nonparametric estimator and a nonparametric test for Granger causality measures that qu...
In this paper, we develop a parametric test procedure for multiple horizon "Granger" causality and a...
The concepts of weak, strong and strict Granger causality are introduced for nonlinear time series m...
Granger-causality in the frequency domain is an emerging tool to analyze the causal relationship be...
Abstract. Granger causality (GC) is one of the most popular measures to re-veal causality influence ...
Abstract Granger causality (GC) has been widely ap-plied in economics and neuroscience to reveal cau...