forecast.cov.estimators.wrt.true
Generate forecasts cov and compare against the output of a true model
Description
Usage
forecast.cov.estimators.wrt.true(true.model, Spawn=.SPAWN, seed=NULL,
simulation.args=NULL,
est.replications=2, pred.replications=2,
discard.before=10, horizons=1:12,quiet=F,
estimation.methods=NULL)
Required Arguments
- true.model
-
An object of class TSmodel.
- estimation.methods
-
A list as used by estimate.models.
Optional Arguments
- simulation.args
-
An arguments to be passed to simulate.
- discard.before
-
An integer indicating the number of points in the
beginning of forecasts to discard for calculating covariances.
- zero
-
If T then forecast.cov is also calculated for a forecast of zero.
- trend
-
If T then forecast.cov is also calculated for a forecast of a linear trend.
- horizons
-
Horizons for which forecast covariance should be calculated.
- horizons
-
Horizons for which forecast covariance should be calculated.
- seed
-
If specified then it is used to set .Random.seed.
- Spawn
-
If T then Splus For loops are used.
- quiet
-
If T then some messages are not printed.
Value
The returned results has element
$forecast.cov.true $forecast.cov.zero $forecast.cov.trend containing
covariances averaged over estimation replications and simulation
replications (forcasts will not change but simulated data will).
$forecast.cov a list of the same length as estimation.methods with each
element containing covariances averaged over estimation replications
and simulation replications.
$estimated.models a list of length est.replications, with each elements as
returned by estimate.models, thus each element has $multi.model as a
subelement containing models for different estimation techniques.
So, eg. $estimated.models[[2]]$multi.model[[1]] in the result will
be the model from the first estimation technique in the second replication.
Details
Calculate the forecasts cov of models estimated from simulations of
true.model with estimation methods indicated by estimation.methods (see
estimate.models).
See Also
Examples
z <- forecast.cov.estimators.wrt.true(model, estimation.methods=list(est.VARX.ls=list(max.lag=4)))
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