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

H. Peter Boswijk, Andre Lucas, Nick Taylor

Research output: Working paperProfessional

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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.
Original languageEnglish
Place of PublicationAmsterdam
PublisherTinbergen Instituut
Publication statusPublished - 1999

Publication series

NameDiscussion paper TI
No.99-012/4

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