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

H. Peter Boswijk (), Andre Lucas and Nick Taylor

No 99-012/4, Tinbergen Institute Discussion Papers from Tinbergen Institute

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.

Date: 1999-02-18
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Citations: View citations in EconPapers (6)

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