dse (version 2020.2-1)

estSSMittnik: Estimate a State Space Model

Description

Estimate a state space model using Mittnik's markov parameter estimation.

Usage

estSSMittnik(data, max.lag=6, n=NULL, subtract.means=FALSE, normalize=FALSE)

Arguments

data

A TSdata object.

max.lag

The number of markov parameters to estimate.

n

The state dimension.

subtract.means

If TRUE subtract the means from the data before estimation.

normalize

If TRUE normalize the data before estimation.

Value

A state space model in an object of class TSestModel.

Details

Estimate a nested-balanced state space model by svd from least squares estimate of markov parameters a la Mittnik (1989, p1195). The quality of the estimate seems to be quite sensitive to max.lag, and this is not properly resolved yet. If n is not supplied the svd criteria will be printed and n prompted for. If subtract.means=T then the sample mean is subtracted. If normalize is T the lsfit estimation is done with outputs normalize to cov=I (There still seems to be something wrong here!!). The model is then re-transformed to the original scale.

See MittnikReduction and references cited there. If the state dimension is not specified then the singular values of the Hankel matrix are printed and the user is prompted for the state dimension.

References

See references for MittnikReduction.

See Also

MittnikReduction estVARXls bft

Examples

# NOT RUN {
    data("egJofF.1dec93.data", package="dse")
    # this prints information about singular values and prompts with
    #Enter the number of singular values to use for balanced model:
    
# }
# NOT RUN {
model <- estSSMittnik(egJofF.1dec93.data)
# }
# NOT RUN {
    # the choice is difficult in this example. 
    model <- estSSMittnik(egJofF.1dec93.data, n=3)
# }