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
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
Date:	2000
Volume:	14
Number of Pages:	23
Page Range:	pp. 25-47
Publication Status:	Published
URI:	http://wrap.warwick.ac.uk/id/eprint/13273

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