forecastCovEstimatorsWRTtrue {dse}R Documentation

Compare Forecasts Cov Relative to True Model Output

Description

Compare covariance of the forecasts less the true model output

Usage

    forecastCovEstimatorsWRTtrue(true.model, rng=NULL,
                       simulation.args=NULL,
                       est.replications = 2, pred.replications = 2,
                       discard.before = 10, horizons = 1:12, quiet =FALSE,
                       estimation.methods=NULL, compiled=.DSEflags()$COMPILED) 

    is.forecastCovEstimatorsWRTtrue(obj)

Arguments

true.model

An object of class TSmodel.

estimation.methods

A list as used by estimateModels.

simulation.args

an arguments to be passed to simulate.

est.replications

An arguments to be passed to simulate.

pred.replications

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.

horizons

Horizons for which forecast covariance should be calculated.

rng

If specified then it is used to set RNG.

quiet

If TRUE then some messages are not printed.

compiled

a logical indicating if the compiled version of the code should be used. (FALSE would typically only be used for debugging.)

obj

an object.

Details

Calculate the forecasts cov of models estimated from simulations of true.model with estimation methods indicated by estimation.methods (see estimateModels). This function makes multiple calls to forecastCovWRTtrue.

Value

The returned results has element forecastCov.true, forecastCov.zero, forecastCov.trend containing covariances averaged over estimation replications and simulation replications (forecasts will not change but simulated data will). forecastCov a list of the same length as estimation.methods with each element containing covariances averaged over estimation replications and simulation replications. estimatedModels a list of length est.replications, with each elements as returned by estimateModels, thus each element has multi.model as a subelement containing models for different estimation techniques. So, eg. estimatedModels[[2]]$multi.model[[1]] in the result will be the model from the first estimation technique in the second replication.

See Also

forecastCovWRTtrue forecastCovEstimatorsWRTdata

Examples

data("eg1.DSE.data.diff", package="dse")
true.model <- estVARXls(eg1.DSE.data.diff) # just to have a starting model
z <-  forecastCovEstimatorsWRTtrue(true.model, 
    estimation.methods=list(estVARXls=list(max.lag=4)))

[Package dse version 2020.2-1 Index]