The usual Wald test for the Granger non-causality in cointegrated vector autoregressive (VAR) processes is known to have the asymptotically non-standard distribution. There have been proposed a few alternative (inefficient) methods which give the asymptotically standard distribution. However,...

In this paper, we propose a new approach to test the hypothesis of long-run Granger non-causality in cointegrated systems. We circumvent the problem of singularity of the variance-covariance matrix associated with the usual Wald type test by proposing a generalized inverse procedure, and an...

It is widely believed that taking cointegration and integration into consideration is useful in constructing long-term forecasts for cointegrated processes. This paper shows that imposing neither cointegration nor integration leads to superior long-term forecasts.

In this paper, we propose a new approach to test the hypothesis of long-run Granger non-causality in cointegrated systems. We circumvent the problem of singularity of the variance-covariance matrix associated with the usual Wald type test by proposing a generalized inverse procedure, and an...

In this article, we propose a new approach to test the hypothesis of long-run Granger non-causality in cointegrated systems. We circumvent the problem of singularity of the covariance matrix associated with the usual Wald-type test by proposing a generalized inverse procedure. A test for the...

This paper proposes an inference procedure for a possibly integrated vector autoregression (VAR) model. We modify the lag augmented VAR (LA-VAR) estimator to exclude the quasiasymptotic bias, which is associated with the term Op(T-1), using the jackknife method. The new estimator has an...