Abstract
Monte Carlo simulations are performed to examine small sample properties of Canonical Cointegrating Regressions (CCR). The first data generation process is designed to generate both cointegrated and non-cointegrated systems with normal disturbances. If the near-observational equivalence of the stationary and the integrated processes is not significant, both powers and empirical sizes of CCR tests are acceptable. The second data generation process is based on the error correction model. Cointegrated systems with various fat-tailed disturbances are generated and analyzed. The empirical sizes of CCR tests with studentt disturbances and GARCH disturbances are found to be reasonable under certain restrictions. The last data generation process is a generalized least squares (GLS) process that incorporates heteroskedasticity into the error correction model. Again, the empirical sizes of CCR tests are reasonable.
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