dse (version 2020.2-1)

selectForecastCov: Select Forecast Covariances Meeting Criteria

Description

Select forecast covariances meeting given criteria.

Usage

selectForecastCov(obj, series=1, 
    select.cov.best=1,
    select.cov.bound=NULL,
    ranked.on.cov.bound=NULL,
    verbose=TRUE)

Arguments

obj

an object as returned by stripMine.

series

an indication of series to which the tests should be applied.

select.cov.best

the number of 'best' forecasts to select.

select.cov.bound

a bound to use as criteria for selection.

ranked.on.cov.bound

see details.

verbose

if verbose=TRUE then summary results are printed.

Value

The returned result is a forecastCov object like obj, but filtered to remove models which do not meet criteria.

Details

Select models with forecast covariance for series meeting criteria. The default select.cov.best=1 selects the best model at each horizon. select.cov.best=3 would select the best 3 models at each horizon. If select.cov.bound is not NULL then select.cov.best is ignored and any model which is better than the bound at all horizons is selected. select.cov.bound can be a vector of the same length as series, in which case corresponding elements are applied to the different series. Any model which is better than the bound at all horizons is selected. ranked.on.cov.bound is is used if it is not NULL and select.cov.bound is NULL. In this case select.cov.best is ignored. ranked.on.cov.bound should be a positive integer. The forecast covariances are ranked by there maximum over the horizon and the lowest number up to ranked.on.cov.bound are selected. This amounts to adjusting the covariance bound to allow for the given number of models to be selected. If series is a vector the results are the best up to the given number on any series! select.cov.bound can be a vector of the same length as series, in which case corresponding elements are applied to the different series. If verbose=TRUE then summary results are printed. The returned result is a forecastCov object like obj, but filtered to remove models which do not meet criteria.

See Also

minForecastCov, excludeForecastCov

Examples

# NOT RUN {
data("eg1.DSE.data.diff", package="dse")
z <- stripMine(eg1.DSE.data.diff, essential.data=c(1,2),
                   estimation.methods=list(estVARXls=list(max.lag=3)))
z <-  selectForecastCov(z)
tfplot(selectForecastCov(z, select.cov.bound=20000))
tfplot(selectForecastCov(z, select.cov.best=1))
# }