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Testing for Co-integration in Vector Autoregressions with Non-Stationary Volatility

Author

Listed:
  • Giuseppe Cavaliere
  • Anders Rahbek
  • A.M.Robert Taylor

    (School of Economics and Management, University of Aarhus, Denmark and CREATES)

Abstract
Many key macro-economic and financial variables are characterised by permanent changes in unconditional volatility. In this paper we analyse vector autoregressions with non-stationary (unconditional) volatility of a very general form, which includes single and multiple volatility breaks as special cases. We show that the conventional rank statistics computed as in Johansen (1988,1991) are potentially unreliable. In particular, their large sample distributions depend on the integrated covariation of the underlying multivariate volatility process which impacts on both the size and power of the associated co-integration tests, as we demonstrate numerically. A solution to the identified inference problem is provided by considering wild bootstrap-based implementations of the rank tests. These do not require the practitioner to specify a parametric model for volatility, nor to assume that the pattern of volatility is common to, or independent across, the vector of series under analysis. The bootstrap is shown to perform very well in practice.

Suggested Citation

  • Giuseppe Cavaliere & Anders Rahbek & A.M.Robert Taylor, 2008. "Testing for Co-integration in Vector Autoregressions with Non-Stationary Volatility," CREATES Research Papers 2008-50, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2008-50
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    More about this item

    Keywords

    Co-integration; non-stationary volatility; trace and maximum eigenvalue tests; wild bootstrap;
    All these keywords.

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • 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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