l.ARMA {dse1} | R Documentation

## Evaluate an ARMA TSmodel

### Description

Evaluate an ARMA TSmodel.

### Usage

        ## S3 method for class 'ARMA':
        l(obj1, obj2, sampleT=NULL, predictT=NULL,result=NULL,
           error.weights=0,  compiled=.DSECOMPILED, warn=TRUE,
           return.debug.info=FALSE, ...)

### Arguments

`obj1` | an 'ARMA' 'TSmodel' object.
`obj2` | a TSdata object.
`sampleT` | an integer indicating the number of periods of data to use.
`predictT` | an integer to what period forecasts should be extrapolated.
`result` | if non-NULL then the returned value is only the sub-element indicated by result. result can be a character string or integer.
`error.weights` | a vector of weights to be applied to the squared prediction errors.
`compiled` | indicates if a call should be made to the compiled code for computation. A FALSE value is mainly for testing purposes.
`warn` | if FALSE then certain warning messages are turned off.
`return.debug.info` | logical indicating if additional debugging information should be returned.
`...` | (further arguments, currently disregarded).

### Details

This function is called by the function l() when the argument to l is an ARMA model. Using l() is usually preferable to calling l.ARMA directly. l.ARMA calls a compiled program unless compiled=FALSE. The compiled version is much faster.

Output data must be at least as long as sampleT. If sampleT is not supplied it is taken to be periods(data).

Input data must be at least as long as predictT. predictT must be at least as large as sampleT. If predictT is not supplied it is taken to be sampleT.

If `error.weights` is greater than zero then weighted prediction errors are calculated up to the horizon indicated by the length of error.weights. The weights are applied to the squared error at each period ahead.

### Value

an object of class TSestModel (see TSestModel).

### See Also

`l`, `l.SS` `TSmodel` `TSestModel.object`

### Examples

        if(is.R()) data("eg1.DSE.data.diff", package="dse1")
        model <- TSmodel(estVARXls(eg1.DSE.data.diff))
        evalutated.model <- l.ARMA(model,eg1.DSE.data.diff)

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