TY  - EJOUR
AU  - Gutknecht, A. J.
AU  - Barnett, L.
TI  - Sampling distribution for single-regression Granger causality estimators
M1  - FZJ-2020-00925
PY  - 2019
N1  - Aaron Gutknecht was employed at the FZJ through the SMARTSTART Training program, project number DB001423.
AB  - We show for the first time that, under the null hypothesis of vanishing Granger causality, the single-regression Granger-Geweke estimator converges to a generalised $\chi^2$ distribution, which may be well approximated by a $\Gamma$ distribution. We show that this holds too for Geweke's spectral causality averaged over a given frequency band, and derive explicit expressions for the generalised $\chi^2$ and $\Gamma$-approximation parameters in both cases. We present an asymptotically valid Neyman-Pearson test based on the single-regression estimators, and discuss in detail how it may be usefully employed in realistic scenarios where autoregressive model order is unknown or infinite. We outline how our analysis may be extended to the conditional case, point-frequency spectral Granger causality, state-space Granger causality, and the Granger causality $F$-test statistic. Finally, we discuss approaches to approximating the distribution of the single-regression estimator under the alternative hypothesis.
LB  - PUB:(DE-HGF)25
UR  - https://juser.fz-juelich.de/record/873702
ER  -