Some Finite Sample Results On Testing For Granger Noncausality

We compare testing strategies for Granger noncausality in vector autoregressions (VARs)that may or may not have unit roots and cointegration. Sequential testing methods are examined; these test for cointegration and use either a differenced VAR or a vector error correction model(VECM), in which to undertake the main noncausality test. Basically, these strategies attempt to verify the validity of appropriate standard limit theory. We contrast such methods with an augmented lag approach that ensures the limiting Chi Square null distribution irrespective of the data’s nonstationarity characteristics. Our simulations involve bivariate and trivariate VARs in which we allow for the lag order to be selected by general to specific testing as well as by model selection criteria. We find that the current practice of pretesting for cointegration can result in severe over-rejections of the noncausal null while overfitting results in better control of the Type I error probability with often little loss in power.