Homogenous panel unit root tests under cross sectional dependence: Finite sample modifications and the wild bootstrap

First generation panel unit root tests are known to be invalid under cross sectional dependence. Focussing on the subclass of homogenous tests, three extensions of existing approaches are proposed. First, a test based on a generalized variance estimator is suggested for panels with small time and relatively large cross sectional dimension. Second, the application of refined residuals in variance estimators is proposed to reduce finite sample biases. Third, the wild bootstrap is proved to be an asymptotically valid method of resampling homogenous panel unit root test statistics. A Monte Carlo study shows that the wild bootstrap yields unbiased inference, even in cases where existing procedures are biased. Most accurate results under the null hypothesis are obtained by resampling robust statistics while there is no, or minor, evidence of power loss invoked by the wild bootstrap. An empirical illustration underpins that the current account to GDP ratio is likely panel stationary.