forecastCovEstimatorsWRTtrue {dse} | R Documentation |

Compare covariance of the forecasts less the true model output

```
forecastCovEstimatorsWRTtrue(true.model, rng=NULL,
simulation.args=NULL,
est.replications = 2, pred.replications = 2,
discard.before = 10, horizons = 1:12, quiet =FALSE,
estimation.methods=NULL, compiled=.DSEflags()$COMPILED)
is.forecastCovEstimatorsWRTtrue(obj)
```

`true.model` |
An object of class TSmodel. |

`estimation.methods` |
A list as used by estimateModels. |

`simulation.args` |
an arguments to be passed to simulate. |

`est.replications` |
An arguments to be passed to simulate. |

`pred.replications` |
An arguments to be passed to simulate. |

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

`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 the compiled version of the code should be used. (FALSE would typically only be used for debugging.) |

`obj` |
an object. |

Calculate the forecasts cov of models estimated from simulations of true.model with estimation methods indicated by estimation.methods (see estimateModels). This function makes multiple calls to forecastCovWRTtrue.

The returned results has element
`forecastCov.true, forecastCov.zero, forecastCov.trend`

containing
covariances averaged over estimation replications and simulation
replications (forecasts will not change but simulated data will).
`forecastCov`

a list of the same length as estimation.methods with each
element containing covariances averaged over estimation replications
and simulation replications.
`estimatedModels`

a list of length est.replications, with each elements as
returned by estimateModels, thus each element has `multi.model`

as a
subelement containing models for different estimation techniques.
So, eg. `estimatedModels[[2]]$multi.model[[1]]`

in the result will
be the model from the first estimation technique in the second replication.

`forecastCovWRTtrue`

`forecastCovEstimatorsWRTdata`

```
data("eg1.DSE.data.diff", package="dse")
true.model <- estVARXls(eg1.DSE.data.diff) # just to have a starting model
z <- forecastCovEstimatorsWRTtrue(true.model,
estimation.methods=list(estVARXls=list(max.lag=4)))
```

[Package *dse* version 2020.2-1 Index]