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A comparison of parametric, semi-nonparametric, adaptive, and nonparametric cointegration tests
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UNSPECIFIED (2000) A comparison of parametric, semi-nonparametric, adaptive, and nonparametric cointegration tests. ADVANCES IN ECONOMETRICS, VOL 14, 14 . pp. 25-47.
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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.
| Item Type: | Journal Article | ||||
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| Subjects: | H Social Sciences > HC Economic History and Conditions H Social Sciences |
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| Series Name: | ADVANCES IN ECONOMETRICS : A RESEARCH ANNUAL | ||||
| Journal or Publication Title: | ADVANCES IN ECONOMETRICS, VOL 14 | ||||
| Publisher: | JAI PRESS INC | ||||
| Official Date: | 2000 | ||||
| Dates: |
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| Volume: | 14 | ||||
| Number of Pages: | 23 | ||||
| Page Range: | pp. 25-47 | ||||
| Publication Status: | Published |
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