Description Usage Arguments Details Value Author(s) See Also Examples
Simulate a TSFmodel to generate time series data (indicators) using factors and loadings from the model.
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model |
A TSFmodel. |
f |
Factors to use with the model. |
Cov |
covariance of the idiosyncratic term. |
sd |
see |
noise |
see |
rng |
see |
noise.model |
see |
... |
arguments passed to other methods. |
simulate.TSFmodel
generates artifical data (indicators or measures)
with a given TSFmodel
(which has factors and loadings). The obj
should be a TSFmodel
. This might be a model constructed with
TSFmodel
or as returned by estTSF.ML
.
The number of factor series is determined by the number of columns in
the time series matrix f
(the factors in the model object).
This must also be the number of columns
in the loadings matrix B (in the model object).
The number of rows in the loadings matrix determines the number of
indicator series generated (the number of columns in the matrix result).
The number of rows in the time series factor matrix determines the number of
time observations (periods) in the indicator series generated, that is, the
number of rows in the matrix result.
simulate
passes Cov
, sd
, noise
, rng
,
and noise.model
to makeTSnoise
to generate
the random idiosyncratic term eps(t),
which will have the same dimension as the generated indicator series that
are returned.
eps(t) will have random distribution determined by other
arguments passed to makeTSnoise
.
Note that the covariance of the generated indicator series y(t)
is also influenced by the covariance of the factors f.
The calculation to give the generated artificial time series indicator data matrix y is
y(t) = B f(t) + eps(t).
simulate.TSFmodel
can use a TSFmodel
that has only
B
and f
specified, but in this case one of Cov
,
sd
, noise
, or noise.model
must be specified as the
default Omega
from the model is not available.
A time series matrix.
Paul Gilbert
TSFmodel
,
estTSF.ML
,
simulate
,
tfplot.TSFmodel
,
explained.TSFmodel
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