sumSqerror {dse1} | R Documentation

## Calculate sum of squared prediction errors

### Description

Calculate a weighted sum squared prediction errors for a parameterization.

### Usage

        sumSqerror(coefficients, model=NULL, data=NULL, error.weights=NULL)

### Arguments

`coefficients` | A vector of coefficients (parameters).
`model` | an object of class TSmodel which gives the structure of the model for which coefficients are used. `coef(model)` should be the same length as coefficients.
`data` | an object of class TSdata which gives the data with which the model is to be evaluated.
`error.weights` | a vector of weights to be applied to the squared prediction errors.

### Details

This function is primarily for use in parameter optimization, which requires that an objective function be specified by a vector of parameters.

### Value

The value of the sum squared errors for a prediction horizon given by the length of error.weights. Each period ahead is weighted by the corresponding weight in error.weights.

### See Also

`l` `l.SS` `l.ARMA`

### Examples

    if(is.R()) data("eg1.DSE.data.diff", package="dse1")
    model <- estVARXls(eg1.DSE.data.diff)
    sumSqerror(1e-10 + coef(model), model=TSmodel(model),
            data=TSdata(model), error.weights=c(1,1,10))

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