Principal Components Analysis of Cointergrated Time Series

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.


Issue Date:
Jun 01 1996
Publication Type:
Working or Discussion Paper
Record Identifier:
http://ageconsearch.umn.edu/record/267775
Language:
English
Total Pages:
44
Series Statement:
Working Paper 2/96




 Record created 2018-02-02, last modified 2018-02-03

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