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.
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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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