dse (version 2003.6-1)

l.SS: Evaluate a state space TSmodel

Description

Evaluate a state space TSmodel.

Usage

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

Arguments

obj1

An 'SS' 'TSmodel' object.

obj2

A TSdata object.

sampleT

an integer indicating the last data point to use for one step ahead filter estimation. If NULL all available data is used.

predictT

an integer indicating how far past the end of the sample predictions should be made. For models with an input, input data must be provided up to predictT. Output data is necessary only to sampleT. If NULL predictT is set to sampleT

error.weights

a vector of weights to be applied to the squared prediction errors.

return.state

if TRUE the element filter$state containing E[z(t)|y(t-1), u(t)] is returned as part of the result. This can be a fairly large matrix.

return.track

if TRUE the element filter$track containing the expectation of the tracking error given y(t-1) and u(t) is returned as part of the result. This can be an very large array.

result

if result is not specified an object of class TSestModel is returned. Otherwise, the specified element of TSestModel$estimates is returned.

compiled

if TRUE the compiled version of the code is used. Otherwise the S/R version is used.

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).

Value

• Usually an object of class TSestModel (see TSestModel), but see result above.

Details

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

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.

See Also

l l.ARMA TSmodel TSestModel.object smoother

Examples

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