EstEval                 package:dse2                 R Documentation

_E_v_a_l_u_a_t_e _a_n _e_s_t_i_m_a_t_i_o_n _m_e_t_h_o_d

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

     Evaluate an estimation method.

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

         EstEval(model, replications=100, rng=NULL, quiet=FALSE, 
                            simulation.args=NULL,
                            estimation=NULL, estimation.args=NULL, 
                            criterion ="coef", criterion.args =NULL, 
                            Spawn=if (exists(".SPAWN")) .SPAWN else FALSE)
         is.EstEval(obj)

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

   model: A TSmodel.

replications: The number of simulations.

     rng: The RNG and starting seed.

   quiet: If TRUE then no information is printed during estimation.

simulation.args: A list of any arguments to pass to simulate.

estimation: A character string indicating the estimation routine to
          use.

estimation.args: A list of any arguments to pass to the estimation
          routine.

criterion: A function to apply to the results of estimation to extract 
          the information which is to be retained.

criterion.args: A list of any arguments to be passed to the criterion
          function.

   Spawn: a logical indication if Splus For loops should be used.

     obj: an object.

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

     estimation.args and criterion.args should be NULL if no args are
     needed. If model is an object of class 'EstEval' or 'simulation'
     then the model and the seed!!! are extracted so the evaluation
     will be based on the same generated sample. criterion can be {
     'coef', 'roots', 'TSmodel', 'TSestModel'} With the default (coef)
     or with TSmodel the other criteria can be reconstructed (when the
     estimation method finds a known form for the model - which is not
     always the case, for example with estBlackBox methods). If
     criterion = 'roots' then criterion.args= list(verbose=FALSE) is
     advised.

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

     A list with element 'result' of length replications, each element
     containing the results of criterion(estimation(simulate(model))).
     Other elements of the list contain information from the supplied
     arguments.

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

     'simulate' 'MonteCarloSimulations' 'distribution'
     'forecastCovWRTtrue'

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

         if(is.R()) data("eg1.DSE.data.diff", package="dse1")
         model <- estVARXls(TSdata(output=outputData(eg1.DSE.data.diff)))
         z <-  EstEval(model, 
            estimation="estVARXls", estimation.args=list(max.lag=2))
         tfplot(z)
         zz <-  EstEval(model, 
            estimation="estVARXls", estimation.args=list(max.lag=2),
            simulation.args=list(sampleT=50, sd=1.5))
         is.EstEval(z)

