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

This paper provides an extensive Monte Carlo comparison of several contempo rary cointegration tests. Apart from the familiar Gaussian-based tests of J ohansen, we also consider tests based on non-Gaussian quasi-likelihoods. Mo reover, we compare the performance of these parametric tests with tests tha t estimate the score function from the data using either kernel estimation or semi-nonparametric density approximations. The comparison is completed w ith a fully nonparametric cointegration test. In small samples, the overall performance of the semi-nonparametric approach appears best in terms of si ze and power. The main cost of the semi-nonparametric approach is the incre ased computation time. In large samples and for heavily skewed or multimoda l distributions, the kernel based adaptive method dominates. For near-Gauss ian distributions, however, the semi-nonparametric approach is preferable a gain.