The Identification of regional convergence clusters: asymptotic versus bootstrap inference

In a recent study Corrado, Martin and Weeks (2005) test for regional convergence across the European Union allowing for an endogenous selection of regional clusters using a multivariate test for stationarity. The number and composition of clusters is determined by the application of pairwise stationarity tests of regional differences in the logarithm of per capita Gross Added Value (GVA). Given that the time series are relatively short, there are potential problems in basing inference on asymptotic results for stationarity tests. To circumvent this problem we bootstrap the stationarity test and explore the robustness of the cluster outcomes. In general our results show that the size distortion which afflicts the asymptotic tests, and resulting in a bias towards finding less convergence, is resolved when we apply the bootstrap generated critical values.