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

Piotr Kębłowski

doi:10.5604/01.3001.0014.0533

Piotr Kębłowski University of Lodz, Faculty of Economics and Sociology, Chair of Econometric Models and Forecasts Przegląd Statystyczny. Statistical Review, vol. 65, 2018, 2, pages: 173-182 Published online: 30 January 2019 DOI 10.5604/01.3001.0014.0533

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ABSTRACT

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.

KEYWORDS

cross-sectional cointegrating vectors, canonical correlation analysis, cointegration tests, panel VAR model, Box and Tiao approach

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