Size and power of cointegration tests with non-normal GARCH error distributions
tests are still more powerful than the alternative tests when the underlying cointegration errors are non-normally distributed. Lee and Tse (1996) examined the performance of Johansen’s tests compared with DF tests and CRDW test when cointegration errors are fitted by GARCH(1,1) model with normal and student-t error distributions. This paper extends
their work. More cointegration tests with a wide range of error distributions are considered. Our simulation results indicate that the performance of power of the Johansen’s tests in capturing cointegration between financial time series is still higher than alternative tests even when the cointegration errors are not normally distributed (a skewed student-t distribution gives the best results). However, the best size performance is given by the Dicky Fuller test using the skewed generalized error distribution.
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