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  \textbf{\Large{\textsc{STANFORD UNIVERSITY}}}\\[5pt]
  \textbf{\Large{\textsc{DEPARTMENT OF STATISTICS}}}\\[5pt]
  \Large{\textsc{DEPARTMENTAL SEMINAR}}
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  4:15 p.m., Tuesday, April 15, 2008\\
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  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
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  \textsl{Qiwei Yao} \\
  London School of Economics
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  \subsection*{Analysing Time Series with Nonstationarity: Common Factors and Curve Series}
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We introduce two methods for modelling time series exhibiting
nonstationarity.

The first method is in the form of the conventional factor model.
However the estimation is carried out via expanding the white noise
space step by step, therefore solving a high-dimensional
optimization problem by many low-dimensional sub-problems. More
significantly it allows the common factors to be nonstationary.
Asymptotic properties of the estimation were investigated. The
proposed methodology was illustrated with both simulated and real
data sets.

The second approach is to accommodate some nonstationary features into
a stationary curved (or functional) time series framework. It turns out
that the stationarity, though defined in a Hilbert space, facilitates
the estimation for the dimension of the curved series in terms of a
standard eigenanalysis.


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