Impulse response functions based on a causal approach to residual orthogonalization in vector autoregressions

A data-determined method for testing structural models of the errors in vector autoregressions is discussed. The method can easily be combined with prior economic knowledge and a subjective analysis of data characteristics to yield valuable information concerning model selection and specification. In one dimension, it turns out that standard t statistics can be used to test the various overidentifying restrictions that are implied by a model. In another dimension, the method compares a priori knowledge of a structural model for the errors with the properties exhibited by the data. Thus this method may help to ensure that orderings of the errors for impulse response and forecast error variance decomposition analyses are sensible, given the data. Two economic examples are used to illustrate the method.