sumSqerror: Calculate sum of squared prediction errors
In dse: Dynamic Systems Estimation (Time Series Package)

Description Usage Arguments Details Value See Also Examples

Calculate a weighted sum squared prediction errors for a parameterization.

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

`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.

This function is primarily for use in parameter optimization, which requires that an objective function be specified by a vector of parameters.It returns only the sum of the weighted squared errors (eg.for optimization). The sample size is determined by TobsOutput(data).

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.

l l.SS l.ARMA

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

Loading required package: tfplot
Loading required package: tframe

Attaching package: ‘dse’

The following objects are masked from ‘package:stats’:

    acf, simulate

[1] 6902.396