Non-Causality Due to Included Variables
Department of Information Engineering, Computer Science and Mathematics University of L’Aquila, 67100 Coppito, Italy
Academic Editor: Marc S. Paolella
Received: 25 May 2017 / Revised: 5 October 2017 / Accepted: 5 October 2017 / Published: 15 October 2017
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
. We want to study causality relationships between the variables in
. Suppose that in a bivariate framework, we find that
Granger causes x
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
. Paradoxically, in this case, it is the non-causality in trivariate system that is misleading.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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MDPI and ACS Style
Triacca, U. Non-Causality Due to Included Variables. Econometrics 2017, 5, 46.
Triacca U. Non-Causality Due to Included Variables. Econometrics. 2017; 5(4):46.
Triacca, Umberto. 2017. "Non-Causality Due to Included Variables." Econometrics 5, no. 4: 46.
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