TSestModel               package:dse1               R Documentation

_E_s_t_i_m_a_t_e_d _T_i_m_e _S_e_r_i_e_s _M_o_d_e_l

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

     Object containing a time series model, data, and estimation
     information.

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

         TSestModel(model)
         is.TSestModel(obj)

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

     This class of objects contains a time series model (TSmodel), 
     data (TSdata), and information obtained by evaluating the model 
     with the data in an element called `estimates' containing:

_l_i_k_e The negative log likelihood function value (a vector of the total,
      constant, the det part, and the cov part)

_c_o_v The estimated residual covariance.

_p_r_e_d The one step ahead predictions (see predictT below). These are 
     aligned with output data so that  residuals are pred[1:sampleT,] -
     output[1:sampleT,]

_s_a_m_p_l_e_T  The end of the period (starting from 1) for which output is
     used for calculating one step ahead predictions.

_p_r_e_d_i_c_t_T  The end of the period for which the model is simulated.
     sampleT must be less than or equal predictT. If predictT is
     greater than sampleT then each step ahead beyond sampleT is based
     on the prediction of the previous step and not  corrected by the
     prediction error.

     The element `estimates' may optionally also contain and  element
     `filter' which may have

_s_t_a_t_e The one step ahead (filter) estimate of the state E[z(t)|y(t-1),
     u(t)]. Note: In the case where there is no input u this
     corresponds to what would usually be called the predicted state -
     not the filtered state.

_t_r_a_c_k The estimated state tracking error P(t|t-1). Again note, this
     corresponds to the predicted tracking eror not the filtered
     tracking error. This is NULL for innovations models.

_s_m_o_o_t_h a list of:

_s_t_a_t_e The smoother (two sided filter) estimate of the state E[z(t)|
     sampleT].

_t_r_a_c_k The smoothed estimate of the state tracking error P(t|sampleT). 
     This is NULL for innovations models.

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

     `TSmodel', `TSdata'

