Template-Type: ReDIF-Paper 1.0
Series: Tinbergen Institute Discussion Papers
Creation-Date: 1999-02-18
Number: 99-012/4
Author-Name: H. Peter Boswijk
Author-Email: h.p.boswijk@uva.nl
Author-Workplace-Name: University of Amsterdam
Author-Name: Andre Lucas
Author-Email: alucas@feweb.vu.nl
Author-Workplace-Name: Vrije Universiteit Amsterdam
Author-Name: Nick Taylor
Author-Workplace-Name: University of Manchester
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.
File-Url: https://papers.tinbergen.nl/99012.pdf
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Handle: RePEc:tin:wpaper:19990012