forecastCovEstimatorsWRTtrue      package:dse2      R Documentation

_C_o_m_p_a_r_e _F_o_r_e_c_a_s_t_s _C_o_v _R_e_l_a_t_i_v_e _t_o _T_r_u_e _M_o_d_e_l _O_u_t_p_u_t

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

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

         forecastCovEstimatorsWRTtrue(true.model, rng=NULL,
                            simulation.args=NULL,
                            est.replications = 2, pred.replications = 2,
                            discard.before = 10, horizons = 1:12, quiet =FALSE,
                            estimation.methods=NULL, compiled=.DSECOMPILED, 
                            Spawn=if (exists(".SPAWN")) .SPAWN else FALSE
                            )
         is.forecastCovEstimatorsWRTtrue(obj)

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

true.model: An object of class TSmodel.

estimation.methods: A list as used by estimateModels.

simulation.args: an arguments to be passed to simulate.

est.replications: An arguments to be passed to simulate.

pred.replications: An arguments to be passed to simulate.

discard.before: An integer indicating the number of points in the
          beginning of forecasts to discard for calculating
          covariances.

horizons: Horizons for which forecast covariance should be calculated.

     rng: If specified then it is used to set RNG.

   Spawn: If TRUE then Splus For loops are used.

   quiet: If TRUE then some messages are not printed.

compiled: a logical indicating if the compiled version of the code
          should be used. (FALSE would typically only be used for
          debugging.)

     obj: an object.

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

     Calculate the forecasts cov of models estimated from simulations
     of  true.model with estimation methods indicated by
     estimation.methods (see  estimateModels).  This function makes
     multiple calls to forecastCovWRTtrue.

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

     The returned results has element 'forecastCov.true,
     forecastCov.zero, forecastCov.trend' containing  covariances
     averaged over estimation replications and simulation replications
     (forecasts will not change but simulated data will). 'forecastCov'
     a list of the same length as estimation.methods with each element
     containing covariances averaged over estimation replications  and
     simulation replications. 'estimatedModels' a list of length
     est.replications, with each elements as returned by
     estimateModels, thus each element has 'multi.model' as a
     subelement containing models for different estimation techniques. 
      So, eg. 'estimatedModels[[2]]$multi.model[[1]]'  in the result
     will be the model from the first estimation technique in the
     second replication.

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

     'forecastCovWRTtrue' 'forecastCovEstimatorsWRTdata'

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

     if(is.R()) data("eg1.DSE.data.diff", package="dse1")
     true.model <- estVARXls(eg1.DSE.data.diff) # just to have a starting model
     z <-  forecastCovEstimatorsWRTtrue(true.model, 
         estimation.methods=list(estVARXls=list(max.lag=4)))

