dse (version 2020.2-1)

forecastCovWRTtrue: Compare Forecasts to True Model Output

Description

Generate forecasts and compare them against the output of a true model.

Usage

forecastCovWRTtrue(models, true.model, 
        pred.replications=1, simulation.args=NULL, quiet=FALSE, rng=NULL, 
	compiled=.DSEflags()$COMPILED,
        horizons=1:12, discard.before=10, trend=NULL, zero=NULL) 

    is.forecastCovWRTdata(obj)

Arguments

models

A list of objects of class TSmodel.

true.model

An object of class TSmodel or TSestModel.

discard.before

An integer indicating the number of points in the beginning of forecasts to discard for calculating covariances.

zero

If TRUE then forecastCov is also calculated for a forecast of zero.

trend

If TRUE then forecastCov is also calculated for a forecast of a linear trend.

pred.replications

integer indicating the number of times simulated data is generated.

simulation.args

A list of any arguments which should be passed to simulate in order to simulate the true model.

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 compiled code should be used. (Usually true except for debugging.)

obj

an object.

Value

A list with the forecast covariance for supplied models on samples generated by the given true model. This is in the element forecastCov of the result. Other elements contain information in the arguments.

Details

The true model is used to generate data and for each generated data set the forecasts of the models are evaluated against the simulated data. If trend is not null it is treated as a model output (forecast) and should be the same dimension as a simulation of the models with simulation.args. If zero is not null a zero forecast is also evaluated. If simulating the true model requires input data then a convenient way to do this is for true.model to be a TSestModel. Otherwise, input data should be passed in simulation.args

See Also

forecastCovEstimatorsWRTdata simulate EstEval distribution MonteCarloSimulations

Examples

# NOT RUN {
data("eg1.DSE.data.diff", package="dse")
true.model <- estVARXls(eg1.DSE.data.diff) # A starting model TSestModel
data <- simulate(true.model)
models <- list(TSmodel(estVARXar(data)),TSmodel(estVARXls(data)))
z <-  forecastCovWRTtrue( models, true.model)
# }