dse (version 2003.6-1)

balanceMittnik: Balance a state space model

Description

Balance a state space model a la Mittnik.

Usage

balanceMittnik(model, n=NULL)
    SVDbalanceMittnik(M, m, n=NULL)

Arguments

model

An TSmodel object.

M

a matrix. See details in MittnikReduction.

m

an integer indicating the number of input series in the model.

n

see details

Value

• A state space model in a TSestModel object.

Details

balanceMittnik calculate a state space model balance a la Mittnik. n is intended primarily for producing a state space model from the markov parameters of an ARMA model, but if it is supplied with an SS model the result will be a model with state dimension n based on the n largest singular values of the svd of a Hankel matrix of markov parameters generated by the original model. If n is not supplied then the singular values are printed and the program prompts for n. balanceMittnik calls SVDbalanceMittnik

SVDbalanceMittnik calculates a nested-balanced state space model by svd a la Mittnik. If state dim n is supplied then svd criteria are not calculated and the given n is used. Otherwise, the singular values are printed and the program prompts for n. M is a matrix with p x (m+p) blocks giving the markov parameters, that is, the first row of the Hankel matrix. It can be generated from the model as in the function markovParms, or from the data, as in the function estSSMittnik. m is the dimension of input series, which is needed to decompose M. The output dimension p is taken from nrow(M).

See also MittnikReduction and references.

References

Gilbert, P. D. (1993) State space and ARMA models: An overview of the equivalence. Working paper 93-4, Bank of Canada. Available at

Gilbert, P.D. (1995) "Combining VAR Estimation and State Space Model Reduction for Simple Good Predictions" J. of Forecasting: Special Issue on VAR Modelling. 14:229-250.

S.Mittnik (1989), Multivariate Time Series Analysis With State Space Models, Computers Math Appl. Vol 17, No 8/9, pp1189-1201. S.Mittnik (1990), Macroeconomic Forecasting Experience With Balance State Space Models, International Journal Of Forecasting, Vol 6, pp337-348. S.Mittnik (1990), Forecasting With Balanced State Space Representations of Multivariate Distributed Lag Models. J. of Forecasting, Vol.9, 207-218.

See Also

estVARXls, estVARXar MittnikReduction

Examples

if(is.R()) data("eg1.DSE.data.diff", package="dse1")
    model <- toSS(TSmodel(estVARXls(eg1.DSE.data.diff)))
    newmodel <-balanceMittnik(model)
    # this prints information about singular values and prompts with
    #Enter the number of singular values to use for balanced model:
    # 18 might be a good choice in this example. 
    newmodel <-balanceMittnik(model, n=18)