select.forecast.cov {dse2} | R Documentation |
Select forecast covariances meeting given criteria.
select.forecast.cov(obj, select.series=1, select.cov.best=1, select.cov.bound=NULL, ranked.on.cov.bound=NULL, verbose=T)
obj |
An object as returned by mine.strip. |
select.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. |
verbose |
If verbose=T then summary results are printed. |
Select models with forecast covariance for select.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 select.series, in which case corresponding elements are applied to the different series.
The returned result is a forecast.cov object like obj, but filtered to remove models which do not meet criteria.
min.forecast.cov
,
exclude.forecast.cov
if(is.R()) data("eg1.DSE.data.diff", package="dse1") z <- mine.strip(eg1.DSE.data.diff, essential.data=c(1,2), estimation.methods=list(est.VARX.ls=list(max.lag=3))) z <- select.forecast.cov(z) tfplot(select.forecast.cov(z, select.cov.bound=20000)) tfplot(select.forecast.cov(z, select.cov.best=1))