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Erscheinungsjahr: 
2017
Quellenangabe: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 5 [Issue:] 4 [Publisher:] MDPI [Place:] Basel [Year:] 2017 [Pages:] 1-4
Verlag: 
MDPI, Basel
Zusammenfassung: 
The contribution of this paper is to investigate a particular form of lack of invariance of causality statements to changes in the conditioning information sets. Consider a discrete-time three-dimensional stochastic process z = (x, y1, y2)0. We want to study causality relationships between the variables in y = (y1, y2)0 and x. Suppose that in a bivariate framework, we find that y1 Granger causes x and y2 Granger causes x, but these relationships vanish when the analysis is conducted in a trivariate framework. Thus, the causal links, established in a bivariate setting, seem to be spurious. Is this conclusion always correct? In this note, we show that the causal links, in the bivariate framework, might well not be 'genuinely' spurious: they could be reflecting causality from the vector y to x. Paradoxically, in this case, it is the non-causality in trivariate system that is misleading.
Schlagwörter: 
Granger causality
Hilbert spaces
time series
JEL: 
C10
C32
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