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

Compare Forecasts to True Model Output

Description

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

Usage

    forecast.cov.wrt.true( models, true.model, 
        pred.replications=1, simulation.args=NULL, quiet=F, 
        rng=NULL, Spawn=.SPAWN, compiled=.DSECOMPILED,
        horizons=1:12, discard.before=10, trend=NULL, zero=NULL)
    is.forecast.cov.wrt.data(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 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.
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.
Spawn If T then Splus For loops are used.
quiet If T then some messages are not printed.

Details

The true model is used to generate more 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

Value

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

See Also

forecast.cov.estimators.wrt.data simulate eval.estimation distribution monte.carlo.simulations

Examples

if(is.R()) data("eg1.DSE.data.diff", package="dse1")
true.model <- est.VARX.ls(eg1.DSE.data.diff) # A starting model TSestModel
data <- simulate(true.model)
models <- list(TSmodel(est.VARX.ar(data)),TSmodel(est.VARX.ls(data)))
z <-  forecast.cov.wrt.true( models, true.model)