forecast.cov.estimators.wrt.true {dse2} | R Documentation |
forecast.cov.estimators.wrt.true(true.model, Spawn=.SPAWN, rng=NULL, simulation.args=NULL, est.replications=2, pred.replications=2, discard.before=10, horizons=1:12,quiet=F, estimation.methods=NULL, compiled=.DSECOMPILED) is.forecast.cov.estimators.wrt.true(obj)
true.model |
An object of class TSmodel. |
estimation.methods |
A list as used by estimate.models. |
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. |
rng |
If specified then it is used to set RNG. |
Spawn |
If T then Splus For loops are used. |
quiet |
If T then some messages are not printed. |
Calculate the forecasts cov of models estimated from simulations of true.model with estimation methods indicated by estimation.methods (see estimate.models). This function makes multiple calls to forecast.cov.wrt.true.
The returned results has element
forecast.cov.true, forecast.cov.zero, forecast.cov.trend
containing
covariances averaged over estimation replications and simulation
replications (forecasts 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.
forecast.cov.wrt.true
forecast.cov.estimators.wrt.data
if(is.R()) data("eg1.DSE.data.diff", package="dse1") true.model <- est.VARX.ls(eg1.DSE.data.diff) # just to have a starting model z <- forecast.cov.estimators.wrt.true(true.model, estimation.methods=list(est.VARX.ls=list(max.lag=4)))