MittnikReduction            package:dse1            R Documentation

_B_a_l_a_n_c_e _a_n_d _R_e_d_u_c_e _a _M_o_d_e_l

_D_e_s_c_r_i_p_t_i_o_n:

     Balance and reduce the state dimension of a state space model a la
     Mittnik.

_U_s_a_g_e:

         MittnikReduction(model, data=NULL, criterion=NULL, verbose=TRUE,warn=TRUE)
         MittnikReduction.from.Hankel(M, data=NULL, nMax=NULL, 
            criterion=NULL, verbose=TRUE, warn=TRUE, 
            Spawn=if (exists(".SPAWN")) .SPAWN else FALSE)

_A_r_g_u_m_e_n_t_s:

   model: An object of class TSmodel or TSestModel.

    data: If the supplied model is of class TSestModel and data is not
          supplied then it is taken from the model. If the model is of
          class TSmodel then data must be supplied.

criterion: Criterion to be used for model  selection. see
          'informationTestsCalculations'.

 verbose: logical indicating if information should be printed  during
          estimation.

    warn: logical indicating if some warning messages should be
          suppressed.

       M: a matrix. See details.

    nMax: integer indicating the state dimension of the largest model
          considered.

   Spawn: logical indicating if Splus For loops should be used.

_D_e_t_a_i_l_s:

     'MittnikReduction' gives nested-balanced state space model using
     reduction by svd of the Hankel matrix generated from a model. If a
     state space model is supplied the max. state dimension for the
     result is taken from the model. If an ARMA model is supplied then
     singular values  will be printed and the program prompts for the
     max. state dimension. criterion should be the name of one of the
     values returned by informationTests, that is, one of
     ("port","like","aic","bic","gvc","rice","fpe","taic", 
     "tbic","tgvc","trice","tfpe"). If criteria is not specified then
     the  program prompts for the state dimension (n) to use for the
     returned model. The program requires data to calculate selection
     criteria. (The program balanceMittnik calculates svd criteria only
     and can be used for reduction without data.) 

     The function 'MittnikReduction.from.Hankel' is called by
     'MittnikReduction' and typically not by the user, but there are
     situations when the former might be called directly. It selects a
     reduced state  space model by svd a la Mittnik. Models and several
     criteria for all  state dimensions up to the max. state dim.
     specified are calculated.  (If nMax is not supplied then svd
     criteria are printed and the program  prompts for nMax). The
     output dimension p is taken from nrow(M). 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.

     'data' is necessary only if criteria (AIC,etc) are to be
     calculated.

_V_a_l_u_e:

     A state space model balance a la Mittnik in an object of class
     TSestModel.

_R_e_f_e_r_e_n_c_e_s:

     Gilbert, P. D. (1993) State space and ARMA models: An overview of
     the equivalence. Working paper 93-4, Bank of Canada. Available at
     <www.bank-banque-canada.ca/pgilbert>

     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.

     Mittnik, S. (1989), Multivariate Time Series Analysis With State 
     Space Models, _Computers Math Appl._ Vol 17, No 8/9, pp1189-1201.

     Mittnik, S. (1990), Macroeconomic Forecasting Experience With 
     Balance State Space Models,   _International Journal Of
     Forecasting_, Vol 6, pp337-348.

     Mittnik, S. (1990), Forecasting With Balanced State Space 
     Representations of Multivariate Distributed Lag Models.  _J. of
     Forecasting_, Vol.9, 207-218.

_S_e_e _A_l_s_o:

     'estVARXls' 'bft' 'balanceMittnik' 'informationTests'
     'informationTestsCalculations'

_E_x_a_m_p_l_e_s:

         if(is.R()) data("egJofF.1dec93.data", package="dse1")
         model <- toSS(estVARXls(egJofF.1dec93.data))
         newmodel <-MittnikReduction(model, criterion="taic")

