Principal Components Analysis of Cointegrated Time Series
研究用主成分方法分析协整时间序列，无需设定误差修正模型或有限阶向量自回归，给出了协整向量的渐近有效估计量及检验方法。
This paper considers the analysis of cointegrated time series using principal components methods. These methods have the advantage of requiring neither the normalization imposed by the triangular error correction model nor the specification of a finite-order vector autoregression. An asymptotically efficient estimator of the cointegrating vectors is given, along with tests forcointegration and tests of certain linear restrictions on the cointegrating vectors. An illustrative application is provided.