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Testing for non-causality by using the Autoregressive Metric

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  • Di Iorio, Francesca
  • Triacca, Umberto

Abstract

A new non-causality test based on the notion of distance between ARMA models is proposed in this paper. The advantage of this test is that it can be used in possible integrated and cointegrated systems, without pre-testing for unit roots and cointegration. The Monte Carlo experiments indicate that the proposed method performs reasonably well in nite samples. The empirical relevance of the test is illustrated via two applications.

Suggested Citation

  • Di Iorio, Francesca & Triacca, Umberto, 2011. "Testing for non-causality by using the Autoregressive Metric," MPRA Paper 29637, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:29637
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    File URL: https://mpra.ub.uni-muenchen.de/29637/2/MPRA_paper_29637.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    AR metric; Bootstrap test; Granger non-causality; VAR;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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