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Bayes multivariate signification tests and Granger causality

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  • Ciuiu, Daniel

Abstract

The Granger causality test is reduced, after co-integration, to the test of the fact that some coefficients of linear regressions are equal to zero or not. In this paper we will build multi-variate Bayes tests for the signification of the parameters of linear regression provided by the above Granger causality, instead of using the classical F statistics. We will consider the cases of known variance, respectively unknown variance. Because we replace in practice the Student tests by the Z tests if the involved number of degrees of freedom is at least 30, we can replace in our paper the case of unknown variance with that of known variance, if the above number of degrees of freedom is at least 30.

Suggested Citation

  • Ciuiu, Daniel, 2011. "Bayes multivariate signification tests and Granger causality," MPRA Paper 48945, University Library of Munich, Germany, revised 01 Oct 2011.
    • Handle: RePEc:pra:mprapa:48945
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    File URL: https://mpra.ub.uni-muenchen.de/48945/1/MPRA_paper_48945.pdf
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    References listed on IDEAS

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    1. Ciuiu, Daniel, 2010. "Informational Criteria for the Homoscedasticity of Errors," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 231-244, July.
    2. Ciuiu, Daniel, 2007. "Bayes, Neyman and Neyman-Bayes Inference for Queueing Systems," MPRA Paper 15049, University Library of Munich, Germany.
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    More about this item

    Keywords

    Bayes multi-variate test; Granger causality;

    JEL classification:

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