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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
Econometrics 2017, 5(4), 46;
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 z = ( x , y 1 , y 2 ) . We want to study causality relationships between the variables in y = ( y 1 , y 2 ) and x. Suppose that in a bivariate framework, we find that y 1 Granger causes x and y 2 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. View Full-Text
Keywords: Granger causality; Hilbert spaces; time series Granger causality; Hilbert spaces; time series
MDPI and ACS Style

Triacca, U. Non-Causality Due to Included Variables. Econometrics 2017, 5, 46.

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