forecastCovWRTtrue {dse} | R Documentation |

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

```
forecastCovWRTtrue(models, true.model,
pred.replications=1, simulation.args=NULL, quiet=FALSE, rng=NULL,
compiled=.DSEflags()$COMPILED,
horizons=1:12, discard.before=10, trend=NULL, zero=NULL)
is.forecastCovWRTdata(obj)
```

`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 TRUE then forecastCov is also calculated for a forecast of zero. |

`trend` |
If TRUE then forecastCov is also calculated for a forecast of a linear trend. |

`pred.replications` |
integer indicating the number of times simulated data is generated. |

`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. |

`quiet` |
If TRUE then some messages are not printed. |

`compiled` |
a logical indicating if compiled code should be used. (Usually true except for debugging.) |

`obj` |
an object. |

The true model is used to generate 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

A list with the forecast covariance for supplied models on samples
generated by the given true model. This is in the element `forecastCov`

of the result. Other elements contain information in the arguments.

`forecastCovEstimatorsWRTdata`

`simulate`

`EstEval`

`distribution`

`MonteCarloSimulations`

```
data("eg1.DSE.data.diff", package="dse")
true.model <- estVARXls(eg1.DSE.data.diff) # A starting model TSestModel
data <- simulate(true.model)
models <- list(TSmodel(estVARXar(data)),TSmodel(estVARXls(data)))
z <- forecastCovWRTtrue( models, true.model)
```

[Package *dse* version 2020.2-1 Index]