Calculate sum of squared prediction errors
Calculate a weighted sum squared prediction errors for a parameterization.
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. |
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.
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))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.