Extracting informative variables in the validation of two-group causal relationship

Ying-Chao Hung () and Neng-Fang Tseng ()

Computational Statistics, 2013, vol. 28, issue 3, 1167 pages

Abstract: The validation of causal relationship between two groups of multivariate time series data often requires the precedence knowledge of all variables. However, in practice one finds that some variables may be negligible in describing the underlying causal structure. In this article we provide an explicit definition of “non-informative variables” in a two-group causal relationship and introduce various automatic computer-search algorithms that can be utilized to extract informative variables based on a hypothesis testing procedure. The result allows us to represent a simplified causal relationship by using minimum possible information on two groups of variables. Copyright Springer-Verlag 2013

Keywords: Causal relationship; Vector autoregression model; Informative variables; Modified Wald test; Automatic computer-search algorithm (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/s00180-012-0351-z

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