Exploring the Impact of Multivariate GARCH Innovations on Hypothesis Testing for Cointegrating Vectors

Takamitsu Kurita
2013 Communications in statistics. Simulation and computation  
This paper 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
more » ... te 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.
doi:10.1080/03610918.2012.677920 fatcat:32ux6u45gvcrli6vobtblvzkmm