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

H. Peter Boswijk (), Andre Lucas () and Nick Taylor
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Nick Taylor: University of Manchester

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

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.

New Economics Papers: this item is included in nep-ecm and nep-ets
Date: 1999-02-18
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