الفهرس 
acknowledgmentOnly 14 pages are availabe for public view
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Abstract
Principal component time series are useful to reduce the
dimension of a multiple time series. The original series are filtered
with a filter that is computed from the Eigen vectors of the spectral
density matrix. The original series is taken here as a vector
autoregressive moving average (VARMA) time series and a vector
bilinear time series. The variance covariance matrix and the spectral
Matrix for the general V ARMA series is derived. The bivariate
Bilinear time series models are studied in details. Their properties
such as existence of stationary solution, ergodicity and inevertibility
are studied. The second order moments are derived and therefore the
spectral matrix is obtained. The principal component time series of
the bivariate models are obtained. A bivariate, or a multiple, bilinear
process can be written as a one dimensional process.