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D.7 Multivariate Time Series 421
used to create a new project. Once you are satisfied with your choices, click OK, and
the simulated series will be generated.
Example D.6.4 To generate a simulated realization of the series AIRPASS.TSM using the current
model and transformed data set, select the option Model>Simulate. The default
options in the dialog box are such as to generate a realization of the original series
as a new project, so it suffices to click OK. You will then see a graph of the simulated
series that should resemble the original series AIRPASS.TSM.
D.7 Multivariate Time Series
D.6.5 Spectral Properties
Spectral properties of both data and fitted ARMA models can also be computed and
plotted with the aid of ITSM. The spectral density of the model is determined by
selecting the option Spectrum>Model. Estimation of the spectral density from observations
of a stationary series can be carried out in two ways, either by fitting an
ARMA model as already described and computing the spectral density of the fitted
model (Section 4.4) or by computing the periodogram of the data and smoothing (Section
4.2). The latter method is applied by selecting the option Spectrum>Smoothed
Periodogram. Examples of both approaches are given in Chapter 4.
Observations {x1,...,xn} of an m-component time series must be stored as an ASCII
file with n rows and m columns, with at least one space between entries in the same
row. To open a multivariate series for analysis, select File>Project>Open>Multivariate
and click OK. Then double-click on the file containing the data, and you will
be asked to enter the number of columns (m) in the data file. After doing this, click
OK, and you will see graphs of each component of the series, with the multivariate
tool bar at the top of the ITSM screen. For examples of the application of ITSM to
the analysis of multivariate series, see Chapter 7.