forecast.cov.estimators.wrt.true {dse2}R Documentation

Compare Forecasts Cov Relative to True Model Output

Description

Usage

    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)

Arguments

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.

Details

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.

Value

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.

See Also

forecast.cov.wrt.true forecast.cov.estimators.wrt.data

Examples

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)))