A Comparison of Parametric, Semi-nonparametric, Adaptive, and Nonparametric Cointegration Tests
AbstractThis 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.
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Date of creation: 18 Feb 1999
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This paper has been announced in the following NEP Reports:
- NEP-ALL-1999-05-03 (All new papers)
- NEP-ECM-1999-05-03 (Econometrics)
- NEP-ETS-1999-05-03 (Econometric Time Series)
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