A comparison of parametric, semi-nonparametric, adaptive and nonparametric tests

H. Peter Boswijk (), Andre Lucas and Nick Taylor

No 62, Serie Research Memoranda from VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics

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.

Keywords: cointegration testing; adaptive estimation; nonparametrics; semi-nonparametrics; Monte-Carlo simulation (search for similar items in EconPapers)
JEL-codes: C14 C32 (search for similar items in EconPapers)
Date: 1998
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:vua:wpaper:1998-62

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