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Factor double autoregressive models with application to simultaneous causality testing

Author

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  • Guo, Shaojun
  • Ling, Shiqing
  • Zhu, Ke

Abstract

Testing causality-in-mean and causality-in-variance has been largely studied. However, none of the tests can detect causality-in-mean and causality-in-variance simultaneously. In this article, we introduce a factor double autoregressive (FDAR) model. Based on this model, a score test is proposed to detect causality-in-mean and causality-in-variance simultaneously. Furthermore, strong consistency and asymptotic normality of the quasi-maximum likelihood estimator (QMLE) for the FDAR model are established. A small simulation study shows good performances of the QMLE and the score test in finite samples. A real data example on the causal relationship between Hong Kong stock market and US stock market is given.

Suggested Citation

  • Guo, Shaojun & Ling, Shiqing & Zhu, Ke, 2013. "Factor double autoregressive models with application to simultaneous causality testing," MPRA Paper 51570, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:51570
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    References listed on IDEAS

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    More about this item

    Keywords

    Asymptotic Normality; Causality-in-mean; Causality-in-variance; Factor DAR model; Instantaneous causality; Score test; Strong consistency.;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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