This paper suggests a combination procedure to exploit the imperfect correlation of cointegration tests to develop a more powerful meta test. To exemplify, we combine Engle and Granger (1987) and Johansen (1988) tests. Either of these underlying tests can be more powerful than the other one...

This paper suggests a combination procedure to exploit the imperfect correlation of cointegration tests to develop a more powerful meta test. To exemplify, we combine Engle and Granger (1987) and Johansen (1988) tests. Either of these underlying tests can be more powerful than the other one...

This paper proposes a new panel unit root test based on Simes' [Biometrika 1986, An Improved Bonferroni Procedure for Multiple Tests of Significanceʺ] classical intersection test. The test is robust to general patterns of cross-sectional dependence and yet straightforward to implement, only...

We use meta analytic combination procedures to develop new tests for panel cointegration. The main idea consists in combining p-values from time series cointegration tests on the different units of the panel. The tests are robust to heterogeneity as well as to cross-sectional dependence between...

Time series cointegration tests, even in the presence of large sample sizes, often yield conflicting conclusions ("mixed signalsʺ) as measured by, inter alia, a low correlation of empirical p-values [see Gregory et al., 2004, Journal of Applied Econometrics]. Using their methodology, we present...