Monte Carlo comparison of LCCA- and ML-based cointegration tests for panel var process with cross-sectional cointegrating vectors

Small-sample properties of bootstrap cointegration rank tests for unrestricted panel VAR process are considered when long-run cross-sectional dependencies occur. It is shown that the bootstrap cointegration rank tests for the panel VAR model based on levels canonical correlation analysis are oversized, whereas the bootstrap cointegration rank tests based on maximum likelihood framework are undersized. Moreover, the former tests are in general outperformed by the latter in terms of performance. The results of the investigation indicate that the ML-based bootstrap cointegration rank tests perform well in small samples for small-sized panel VAR models with a few cross-sections.