@techreport{7c755ea32fc74d5cb8aafaee4c8ef904,
title = "A Comparison of Parametric, Semi-nonparametric, Adaptive, and Nonparametric Cointegration Tests",
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.",
author = "Boswijk, {H. Peter} and Andre Lucas and Nick Taylor",
year = "1999",
language = "English",
series = "Discussion paper TI",
publisher = "Tinbergen Instituut",
number = "99-012/4",
type = "WorkingPaper",
institution = "Tinbergen Instituut",
}