MonteCarloSimulations {dse2} | R Documentation

## Generate simulations

### Description

Run multiple simulations

### Usage

        is.MonteCarloSimulations(obj)
        MonteCarloSimulations(model, simulation.args=NULL,
               replications=100, rng=NULL, quiet =FALSE, ...)
        ## Default S3 method:
        MonteCarloSimulations(model, simulation.args = NULL,
                    replications = 100, rng = NULL, quiet =FALSE, ...)
        ## S3 method for class 'TSmodel':
        MonteCarloSimulations(model, simulation.args=NULL,
              replications=100, rng=NULL, quiet=FALSE,
              Spawn=if (exists(".SPAWN")) .SPAWN else FALSE, ...)
        ## S3 method for class 'TSestModel':
        MonteCarloSimulations(model, simulation.args=NULL,
               replications=100, rng=NULL, quiet=FALSE, ...)
        ## S3 method for class 'EstEval':
        MonteCarloSimulations(model, simulation.args=NULL,
                replications=100, rng=getRNG(model),  quiet=FALSE, ...)
        ## S3 method for class 'MonteCarloSimulations':
        MonteCarloSimulations(model,
           simulation.args=NULL, replications=100, rng=getRNG(model),  quiet=FALSE, ...)

### Arguments

`model` | an object from which a model can be extracted. The model must have an associated `simulation` method (e.g. a TSmodel).
`simulation.args,` | A list of arguments in addition to model which are passed to simulate.
`replications` | The number of simulations.
`rng` | The RNG and starting seed.
`quiet` | logical indicating if printing and many warning messages should be suppressed.
`Spawn` | If TRUE "For" loops are used in Splus.
`obj` | an object.
`...` | arguments passed to other methods.

### Details

This function runs many simulations using `simulate`. Often it not be necessary to do this since the seed can be used to reproduce the sample and many functions for testing estimation methods, etc., will produce samples as they proceed. This function is useful for verification and for looking at the stochastic properties of the output of a model. If `model` is an object of class `EstEval` or `simulation` then the model and the seed!!! are extracted so the same sample will be generated. The default method expects the result of `simulate(model)` to be a matrix. There is a `tfplot` method (time series plots of the simulations) and a `distribution` method for the result. The latter plots kernel estimates of the distribution of the simulations at specified periods.

### Value

A list of simulations.

### See Also

`simulate` `EstEval` `distribution` `forecastCovWRTtrue`

### Examples

    if(is.R()) data("eg1.DSE.data.diff", package="dse1")
    model <- estVARXls(eg1.DSE.data.diff)
    z <-  MonteCarloSimulations(model, simulation.args=list(sampleT=100))
    tfplot(z)
    distribution(z)

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