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

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  • Ying-Chao Hung
  • Neng-Fang Tseng

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

Suggested Citation

  • Ying-Chao Hung & Neng-Fang Tseng, 2013. "Extracting informative variables in the validation of two-group causal relationship," Computational Statistics, Springer, vol. 28(3), pages 1151-1167, June.
    • Handle: RePEc:spr:compst:v:28:y:2013:i:3:p:1151-1167
    DOI: 10.1007/s00180-012-0351-z
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    1. Jan T. A. Koster, 1999. "On the Validity of the Markov Interpretation of Path Diagrams of Gaussian Structural Equations Systems with Correlated Errors," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 26(3), pages 413-431, September.
    2. R. Scott Hacker & Abdulnasser Hatemi-J, 2006. "Tests for causality between integrated variables using asymptotic and bootstrap distributions: theory and application," Applied Economics, Taylor & Francis Journals, vol. 38(13), pages 1489-1500.
    3. Jean-Marie Dufour & Eric Renault, 1998. "Short Run and Long Run Causality in Time Series: Theory," Econometrica, Econometric Society, vol. 66(5), pages 1099-1126, September.
    4. Granger, C. W. J., 1980. "Testing for causality : A personal viewpoint," Journal of Economic Dynamics and Control, Elsevier, vol. 2(1), pages 329-352, May.
    5. 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.
    6. Osborn, Denise R, 1984. "Causality Testing and Its Implications for Dynamic Econometric Models," Economic Journal, Royal Economic Society, vol. 94(376a), pages 82-96, Supplemen.
    7. Lutkepohl, Helmut & Burda, Maike M., 1997. "Modified Wald tests under nonregular conditions," Journal of Econometrics, Elsevier, vol. 78(2), pages 315-332, June.
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    Cited by:

    1. Long, Ting-Hsuan & Emura, Takeshi, 2014. "A control chart using copula-based Markov chain models," MPRA Paper 57419, University Library of Munich, Germany.

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