Permanent‐transitory Decomposition in Var Models With Cointegration and Common Cycles
推导了具有协整和序列相关共同特征的高斯VAR(p)模型中非平稳多元时间序列的永久-暂时分解，扩展了现有分析至两类降秩结构，并通过美国商业周期实证展示了方法的实用性。
In this paper we derive permanent‐transitory decompositions of non‐stationary multiple times series generated by (r)nite order Gaussian VAR(p) models with both cointegration and serial correlation common features. We extend existing analyses to the two classes of reduced rank structures discussed in Hecq, Palm and Urbain (1998). Using the corresponding state space representation of cointegrated VAR models in vector error correction form we show how decomposition can be obtained even in the case where the number of common feature and cointegration vectors are not equal to the number of variables. As empirical analysis of US business fluctuations shows the practical relevance of the approach we propose.