Principal Components Analysis of Cointegrated Time Series
journal contributionposted on 05.06.2017 by Harris, David
Any type of content formally published in an academic journal, usually following a peer-review process.
This paper considers the analysis of cointegrated time series using principal components methods. These methods have the advantage of neither requiring the normalisation 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 for cointegration and tests of certain linear restrictions on the cointegrating vectors. An illustrative application is provided.