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A General Representation Theorem for Integrated Vector Autoregressive Processes

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  • Massimo Franchi

    (Department of Economics, University of Copenhagen)

Abstract

We study the algebraic structure of an I(d) vector autoregressive process, where d is restricted to be an integer. This is useful to characterize its polynomial cointegrating relations and its moving average representation, that is to prove a version of the Granger representation theorem valid for I(d) vector autoregressive processes.

Suggested Citation

  • Massimo Franchi, 2006. "A General Representation Theorem for Integrated Vector Autoregressive Processes," Discussion Papers 06-16, University of Copenhagen. Department of Economics.
    • Handle: RePEc:kud:kuiedp:0616
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    File URL: http://www.econ.ku.dk/english/research/publications/wp/2006/0616.pdf/
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    References listed on IDEAS

    as
    1. la Cour, Lisbeth, 1998. "A Parametric Characterization Of Integrated Vector Autoregressive (Var) Processes," Econometric Theory, Cambridge University Press, vol. 14(2), pages 187-199, April.
    2. Gregoir, Stephane & Laroque, Guy, 1994. "Polynomial cointegration estimation and test," Journal of Econometrics, Elsevier, vol. 63(1), pages 183-214, July.
    3. Johansen, Søren, 1992. "A Representation of Vector Autoregressive Processes Integrated of Order 2," Econometric Theory, Cambridge University Press, vol. 8(2), pages 188-202, June.
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    Cited by:

    1. Arbués, Ignacio & Ledo, Ramiro & Matilla-García, Mariano, 2016. "Automatic identification of general vector error correction models," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 10, pages 1-41.

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    More about this item

    Keywords

    vector autoregressive processes; unit roots; Granger representation theorem; cointegration;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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