selectForecastCov            package:dse2            R Documentation

_S_e_l_e_c_t _F_o_r_e_c_a_s_t _C_o_v_a_r_i_a_n_c_e_s _M_e_e_t_i_n_g _C_r_i_t_e_r_i_a

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

     Select forecast covariances meeting given criteria.

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

         selectForecastCov(obj, series=1, 
         select.cov.best=1,
         select.cov.bound=NULL,
         ranked.on.cov.bound=NULL,
         verbose=TRUE)

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

     obj: an object as returned by stripMine.

  series: an indication of series to which the tests should be applied.

select.cov.best: the number of 'best' forecasts to select.

select.cov.bound: a bound to use as criteria for selection.

ranked.on.cov.bound: see details.

 verbose: if verbose=TRUE then summary results are printed.

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

     Select models with forecast covariance for 'series' meeting
     criteria. The default 'select.cov.best=1' selects the best model
     at each horizon. 'select.cov.best=3' would select the best 3
     models at each horizon. If 'select.cov.bound' is not 'NULL' then 
     'select.cov.best' is ignored and any model which is better than
     the bound at all horizons is selected. 'select.cov.bound' can be a
     vector of the same length  as 'series', in which case
     corresponding elements are applied to the different series. Any
     model which is better than the bound at all horizons is selected.
     'ranked.on.cov.bound' is is used if it is not NULL and 
     'select.cov.bound' is 'NULL'. In this case 'select.cov.best' is
     ignored. 'ranked.on.cov.bound' should be a positive integer. The
     forecast covariances are ranked by there maximum over the horizon
     and the lowest number up to 'ranked.on.cov.bound' are selected.
     This amounts to adjusting the covariance bound to allow for the
     given number of models to be selected. If 'series' is a vector the
     results are  the best up to the given number on any series!
     'select.cov.bound' can be a vector of the same length as 
     'series', in which case corresponding elements are applied to the
     different series. If 'verbose=TRUE' then summary results are
     printed. The returned result is a 'forecastCov' object like obj,
     but filtered to remove models which do not meet criteria.

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

     The returned result is a forecastCov object like obj, but filtered
     to remove models which do not meet criteria.

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

     'minForecastCov', 'excludeForecastCov'

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

     if(is.R()) data("eg1.DSE.data.diff", package="dse1")
     z <- stripMine(eg1.DSE.data.diff, essential.data=c(1,2),
                        estimation.methods=list(estVARXls=list(max.lag=3)))
     z <-  selectForecastCov(z)
     tfplot(selectForecastCov(z, select.cov.bound=20000))
     tfplot(selectForecastCov(z, select.cov.best=1))

