Impacts of multivariate GARCH innovations on hypothesis testing for cointegrating vectors
نویسنده
چکیده
This note investigates impacts of multivariate generalised autoregressive conditional heteroskedasticity (GARCH) errors on hypothesis testing for cointegrating vectors. The study reviews a cointegrated vector autoregressive model incorporating multivariate GARCH innovations and a regularity condition required for valid asymptotic inferences. Monte Carlo experiments are then conducted on a test statistic for a hypothesis on the cointegrating vectors. The experiments demonstrate that the regularity condition plays a crucial role in rendering the hypothesis testing operational. It is also shown that the Bartlett correction and wild bootstrapping are useful in improving the small-sample performance of the test statistic of interest. Keywords: Cointegrating vector, Multivariate GARCH, Monte Carlo experiment, Bartlett correction, Wild bootstrapping. JEL Classi cation Codes: C32, C52, C63.
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