Principal Components Analysis of Cointegrated Time Series
AbstractThis 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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Bibliographic InfoArticle provided by Cambridge University Press in its journal Econometric Theory.
Volume (Year): 13 (1997)
Issue (Month): 04 (August)
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Other versions of this item:
- Harris, D., 1996. "Principal Components Analysis of Cointegrated Time Series," Monash Econometrics and Business Statistics Working Papers 2/96, Monash University, Department of Econometrics and Business Statistics.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
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