selectForecastCov {dse2} | R Documentation

## 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.

### 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.

### Value

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

### See Also

`minForecastCov`, `excludeForecastCov`

### Examples

    if(is.R()) data("eg1.DSE.data.diff", package="dse1")
    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))

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