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A Comparison of Parametric, Semi-nonparametric, Adaptive, and Nonparametric Cointegration Tests

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  • H. Peter Boswijk

    (University of Amsterdam)

  • Andre Lucas

    (Vrije Universiteit Amsterdam)

  • Nick Taylor

    (University of Manchester)

Abstract

This paper provides an extensive Monte-Carlo comparison of severalcontemporary cointegration tests. Apart from the familiar Gaussian basedtests of Johansen, we also consider tests based on non-Gaussianquasi-likelihoods. Moreover, we compare the performance of these parametrictests with tests that estimate the score function from the data using eitherkernel estimation or semi-nonparametric density approximations. Thecomparison is completed with a fully nonparametric cointegration test. Insmall samples, the overall performance of the semi-nonparametric approachappears best in terms of size and power. The main cost of thesemi-nonparametric approach is the increased computation time. In largesamples and for heavily skewed or multimodal distributions, the kernel basedadaptive method dominates. For near-Gaussian distributions, however, thesemi-nonparametric approach is preferable again.

Suggested Citation

  • H. Peter Boswijk & Andre Lucas & Nick Taylor, 1999. "A Comparison of Parametric, Semi-nonparametric, Adaptive, and Nonparametric Cointegration Tests," Tinbergen Institute Discussion Papers 99-012/4, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:19990012
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    References listed on IDEAS

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    Cited by:

    1. Kirstin Hubrich & Helmut Lutkepohl & Pentti Saikkonen, 2001. "A Review Of Systems Cointegration Tests," Econometric Reviews, Taylor & Francis Journals, vol. 20(3), pages 247-318.
    2. Krauss, Christopher & Herrmann, Klaus & Teis, Stefan, 2015. "On the power and size properties of cointegration tests in the light of high-frequency stylized facts," FAU Discussion Papers in Economics 11/2015, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    3. Christopher Krauss & Klaus Herrmann, 2017. "On the Power and Size Properties of Cointegration Tests in the Light of High-Frequency Stylized Facts," JRFM, MDPI, vol. 10(1), pages 1-24, February.
    4. Martin Wagner, 2004. "A Comparison of Johansen's, Bierens’ and the Subspace Algorithm Method for Cointegration Analysis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(3), pages 399-424, July.
    5. Giulio Cifarelli & Giovanna Paladino, 2008. "Reserve overstocking in a highly integrated world. New evidence from Asia and Latin America," The European Journal of Finance, Taylor & Francis Journals, vol. 14(4), pages 315-336.
    6. David O. Cushman, 2003. "Further evidence on the size and power of the Bierens and Johansen cointegration procedures," Economics Bulletin, AccessEcon, vol. 3(25), pages 1-7.

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