A0892
Title: Adaptive information-based methods for determining the co-integration rank in heteroskedastic VAR models
Authors: Luca De Angelis - University of Bologna (Italy) [presenting]
Giuseppe Cavaliere - University of Bologna (Italy)
Robert Taylor - University of Essex (United Kingdom)
Peter Boswijk - University of Amsterdam (Netherlands)
Abstract: Standard methods for determining the co-integration rank of vector autoregressive (VAR) systems of variables integrated of order one are affected by the presence of heteroskedasticity with sequential procedures based on Johansen's (pseudo-) likelihood ratio test being significantly over-sized in finite samples and even asymptotically. Notable solutions to this problem are the wild bootstrap applied to the traditional likelihood ratio test or an information criterion such as standard BIC. However, although asymptotically valid, these methods may show low power in small samples as they do not exploit the potential efficiency gains provided by the adaptation with respect to the volatility process. Therefore, adaptive methods where the covariance matrix is estimated nonparametrically can be particularly useful in the determination of the co-integration rank in VAR models driven by heteroskedastic innovations as they exploit the power gain potential, especially in the presence of nonstationary unconditional volatility. We show that adaptive information criteria are weakly consistent provided the usual conditions on the penalty term hold and display better finite sample results in many situations.
