A general nonparametric bootstrap test for Granger causality

By Cees G. H. Diks, J. Degoede. 2001

72 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e451f5e198fa6242a298b85c96ed8addb3be4f33

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In Statistics and Probability

Citations in the corpus

Forecasting UK Industrial Production with Multivariate Singular Spectrum Analysis (2013, methodology): “A method based on the information theory has realized a more general Granger causality measure that accommodates in principle arbitrary statistical processes [46].”
Causal independence between energy consumption and economic growth in Liberia: Evidence from a non-parametric bootstrapped causality test (2012, methodology): “Third, parametric tests based on prediction errors will be sensitive only to causality in the mean, while higher order structure such as heteroskedasticity, will be ignored (see Diks and DeGoede, 2001; Diks and Panchenko, 2006).”
A Note on the Hiemstra-Jones Test for Granger Non-causality (2005, background): “The fact that the sizes reported in the bootstrap study by Diks and DeGoede (2001) were close to nominal is related to the relatively small sample sizes used there.”
Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity (2010, methodology): “Therefore, like in Diks and DeGoede (2001) and Theiler et al. (1992), we use bootstrapping with surrogate data to find the empirical distributions of each statistic under the null hypothesis.”
The electricity consumption versus economic growth of the Polish economy (2012, background): “Secondly, since linear methods depend on testing the significance of suitable parameters only in a mean equation, causality in any higher–order structure (like causality in variance etc.) cannot be explored (Diks and DeGoede, 2001).”