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Non-Causality Due to Included Variables

Author

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  • Umberto Triacca

    (Department of Information Engineering, Computer Science and Mathematics University of L’Aquila, 67100 Coppito, Italy)

Abstract

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.

Suggested Citation

  • Umberto Triacca, 2017. "Non-Causality Due to Included Variables," Econometrics, MDPI, vol. 5(4), pages 1-4, October.
    • Handle: RePEc:gam:jecnmx:v:5:y:2017:i:4:p:46-:d:115044
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    References listed on IDEAS

    as
    1. Lutkepohl, Helmut, 1982. "Non-causality due to omitted variables," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 367-378, August.
    2. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
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    Cited by:

    1. Gulasekaran Rajaguru & Michael O’Neill & Tilak Abeysinghe, 2018. "Does Systematic Sampling Preserve Granger Causality with an Application to High Frequency Financial Data?," Econometrics, MDPI, vol. 6(2), pages 1-24, June.

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