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  • 标题:Non-Causality Due to Included Variables
  • 本地全文:下载
  • 作者:Triacca, Umberto
  • 期刊名称:Econometrics
  • 印刷版ISSN:2225-1146
  • 出版年度:2017
  • 卷号:5
  • 期号:4
  • 页码:1-4
  • 出版社:MDPI, Open Access Journal
  • 摘要: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.
  • 关键词:Granger causality; Hilbert spaces; time series
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