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Semi-nonparametric cointegration testing

Author

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  • Boswijk, H. Peter

    (Vrije Universiteit Amsterdam, Faculteit der Economische Wetenschappen en Econometrie (Free University Amsterdam, Faculty of Economics Sciences, Business Administration and Economitrics)

  • Lucas, André

Abstract

This paper considers a semi-nonparametric cointegration test. The test uses the LM-testing principle. The score function needed for the LM-test is estimated from the data using an expansion of the density around a Student t distribution. In this way, we capture both the possible fat-tailedness and the skewness of the innovation process. Model selection criteria are employed to select the appropriate order of the expansion in finite samples. Using a Monte-Carlo experiment, we show that the semi-nonparametric cointegration test has good size and power properties. The test outperforms previous testing procedures in terms of power over a broad class of distributions for the innovation process.

Suggested Citation

  • Boswijk, H. Peter & Lucas, André, 1997. "Semi-nonparametric cointegration testing," Serie Research Memoranda 0041, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    • Handle: RePEc:vua:wpaper:1997-41
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    References listed on IDEAS

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    Cited by:

    1. Bo Zhou, 2023. "Semiparametrically Optimal Cointegration Test," Papers 2305.08880, arXiv.org.
    2. Ted Juhl & Zhijie Xiao, 2000. "N-Consistent Semiparametric Regression: Partially Linear Models with Unit Roots," Econometric Society World Congress 2000 Contributed Papers 1532, Econometric Society.
    3. H. Peter Boswijk, 2000. "Testing for a Unit Root with Near-Integrated Volatility," Econometric Society World Congress 2000 Contributed Papers 1101, Econometric Society.
    4. H. Peter Boswijk & Franc Klaassen, 2005. "Why Frequency Matters for Unit Root Testing," Tinbergen Institute Discussion Papers 04-119/4, Tinbergen Institute.
    5. Kirstin Hubrich & Helmut Lutkepohl & Pentti Saikkonen, 2001. "A Review Of Systems Cointegration Tests," Econometric Reviews, Taylor & Francis Journals, vol. 20(3), pages 247-318.
    6. Lanne, Markku & Lütkepohl, Helmut, 2010. "Structural Vector Autoregressions With Nonnormal Residuals," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 159-168.
    7. Boswijk, H. Peter & Lucas, André & Taylor, Nick, 1998. "A comparison of parametric, semi-nonparametric, adaptive and nonparametric tests," Serie Research Memoranda 0062, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    8. 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.
    9. Nikolaus A. Siegfried, 2002. "An information-theoretic extension to structural VAR modelling," Quantitative Macroeconomics Working Papers 20203, Hamburg University, Department of Economics.
    10. Juhl, Ted & Xiao, Zhijie, 2005. "Testing for cointegration using partially linear models," Journal of Econometrics, Elsevier, vol. 124(2), pages 363-394, February.
    11. Jurgen A. Doornik & H. Peter Boswijk, 2005. "Distribution approximations for cointegration tests with stationary exogenous regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(6), pages 797-810.
    12. Al-Sadoon, Majid M., 2017. "A unifying theory of tests of rank," Journal of Econometrics, Elsevier, vol. 199(1), pages 49-62.

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    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General

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