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Principal Components Analysis of Cointegrated Time Series

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Author Info
Harris, D.
Abstract

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 eror correction model, nor the specification of a finite order vector autoregression.

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Publisher Info
Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 2/96.

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Length: 43 pages
Date of creation: 1996
Date of revision:
Handle: RePEc:msh:ebswps:1996-2

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Related research
Keywords: COINTEGRATION; TIME SERIES; EVALUATION;

Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data

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