estVARXls                package:dse1                R Documentation

_E_s_t_i_m_a_t_e _a _V_A_R _T_S_m_o_d_e_l

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

     Estimate a VAR TSmodel with (optionally) an exogenous input and 
     (optionally) a trend.

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

         estVARXls(data, subtract.means=FALSE, re.add.means=TRUE, standardize=FALSE, 
          unstandardize=TRUE, max.lag=NULL, trend=FALSE, lag.weight=1.0, warn=TRUE) 

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

    data: A TSdata object.

subtract.means: If TRUE subtract the means from the data before
          estimation.

re.add.means: If TRUE and subtract.means is TRUE then the mean is added
          back to the data and  the model is adjusted for the non-zero
          mean data when returned.

standardize: If TRUE divide each series by its sample standard 
          deviation before estimation. Note that the mean is not
          subtracted unless subtract.means is TRUE.

unstandardize: If TRUE and standardize is TRUE then the returned  model
          is adjusted to correspond to the original data.

   trend: If TRUE a trend is estimated.

 max.lag: Number of lags to be used.

lag.weight: Weight between 0 and 1 to be applied to lagged data.  Lower
          weights mean lagged data is less important (more noisy).

    warn: If TRUE a warning message is issued when missing data (NA) is
          detected and the model predictions are reconstructed from the
          lsfit residuals.

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

     A VAR model is fitted by least squares regression using lsfit. The
     argument max.lag determines the number of lags. If a trend is not
     estimated the function estVARXar may be preferred. Missing data is
     allowed in lsfit, but not (yet) by ARMA which generates the model
     predictions, etc., based on the estimated model and the data.
     (This is done to ensure the result is consistent with other
     estimation techniques.) In the case of missing data ARMA is not
     used and the model predictions, etc., are generated by adding the
     data and the lsfit residual. This is slightly different from using
     ARMA, especially with respect to initial conditions.

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

     A TSestModel object containing a TSmodel object which is a VAR
     model.

_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.

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

     'estSSfromVARX' 'estSSMittnik' 'bft' 'estVARXar'

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

         if(is.R()) data("eg1.DSE.data.diff", package="dse1")
         model <- estVARXls(eg1.DSE.data.diff)

