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A comparison of parametric, semi-nonparametric, adaptive and nonparametric tests

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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
  • Taylor, Nick

Abstract

This paper provides an extensive Monte-Carlo comparison of several contemporary cointegration tests. Apart from the familiar Gaussian based tests of Johansen, we also consider tests based on non-Gaussian quasi-likelihoods. Moreover, we compare the performance of these parametric tests with tests that estimate the score function from the data using either kernel estimation or semi-nonparametric density approximations. The comparison is completed with a fully nonparametric cointegration test. In small samples, the overall performance of the semi-nonparametric approach appears best in terms of size and power. The main cost of the semi-nonparametric approach is the increased computation time. In large samples and for heavily skewed or multimodal distributions, the kernel based adaptive method dominates. For near-Gaussian distributions, however, the semi-nonparametric approach is preferable again.

Suggested Citation

  • 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.
    • Handle: RePEc:vua:wpaper:1998-62
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    References listed on IDEAS

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    1. Phillips, Peter C B, 1988. "Regression Theory for Near-Integrated Time Series," Econometrica, Econometric Society, vol. 56(5), pages 1021-1043, September.
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    5. Gallant, A. Ronald & Tauchen, George, 1997. "Reprojecting Partially Observed Systems with Application to Interest Rate Diffusions," Working Papers 97-09, Duke University, Department of Economics.
    6. Franses, Philip Hans & Lucas, Andre, 1998. "Outlier Detection in Cointegration Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(4), pages 459-468, October.
    7. Gallant, A Ronald & Nychka, Douglas W, 1987. "Semi-nonparametric Maximum Likelihood Estimation," Econometrica, Econometric Society, vol. 55(2), pages 363-390, March.
    8. Franses, Philip Hans & Kloek, Teun & Lucas, Andre, 1998. "Outlier robust analysis of long-run marketing effects for weekly scanning data," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 293-315, November.
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    11. Boswijk, H. Peter & Lucas, Andre, 2002. "Semi-nonparametric cointegration testing," Journal of Econometrics, Elsevier, vol. 108(2), pages 253-280, June.
    12. Bierens, Herman J., 1997. "Nonparametric cointegration analysis," Journal of Econometrics, Elsevier, vol. 77(2), pages 379-404, April.
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    Cited by:

    1. 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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    More about this item

    Keywords

    cointegration testing; adaptive estimation; nonparametrics; semi-nonparametrics; Monte-Carlo simulation;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: 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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