Impulse Response Functions Based on a Causal Approach to Residual Orthogonalization in Vector Autoregressions
提出一种数据驱动方法，用于检验向量自回归中误差的结构模型，可结合先验经济知识和数据特征，帮助确定脉冲响应分析中误差的合理排序。
Abstract 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.