Principal Components Analysis of Cointegrated Time Series
Principal Components Analysis of Cointegrated Time Series
复制标题
协整时间序列的主成分分析
作者：
D. Harris
本文考虑用主成分法对协整时间序列进行分析。这些方法的优点是既不需要三角误差修正模型的归一化，也不需要有限阶向量自回归的说明。给出了协整向量的渐近有效估计，并给出了协整向量的检验和对协整向量的若干线性限制的检验。提供了一个说明性应用程序。
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.