A comparison of parametric, semi-nonparametric, adaptive, and nonparametric cointegration tests
UNSPECIFIED (2000) A comparison of parametric, semi-nonparametric, adaptive, and nonparametric cointegration tests. ADVANCES IN ECONOMETRICS, VOL 14, 14 . pp. 25-47.Full text not available from this repository.
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.
|Item Type:||Journal Article|
|Subjects:||H Social Sciences > HC Economic History and Conditions
H Social Sciences
|Series Name:||ADVANCES IN ECONOMETRICS : A RESEARCH ANNUAL|
|Journal or Publication Title:||ADVANCES IN ECONOMETRICS, VOL 14|
|Publisher:||JAI PRESS INC|
|Number of Pages:||23|
|Page Range:||pp. 25-47|
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