estVARXar                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.

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

         estVARXar(data, subtract.means=FALSE,  re.add.means=TRUE, standardize=FALSE, 
              unstandardize=TRUE, aic=TRUE, max.lag=NULL, method="yule-walker", 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 the model is adjusted for the non-zero mean data
          when returned.  If subtract.means is also TRUE then the mean
          is added back to the data.

standardize: Note that the mean is not subtracted unless subtract.means
          is TRUE. A VAR model in an object of class TSestModel.

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

     aic: Passed to function ar.

 max.lag: The maximum number of lags that should be considered.

  method: Passed to function ar.

    warn: If TRUE certain warning message are suppressed.

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

     This function estimates a VAR model with exogenous variable using
     ar(). Residuals,etc, are calculated by evaluating the estimated
     model with ARMA. The procedure ar is used by combine exogeneous
     variables and endogenous variable and estimating as if all
     variables were endogenous. The estVARXar  method does not support
     trend estimation (as in estVARXls).

     If aic=TRUE the number of lags is determined by an AIC statistic
     (see ar).  If an exogenous (input)  variable is supplied the input
     and output are combined (i.e.- both treated as outputs) for
     estimation, and the resulting model is converted back by
     transposing the exogenous variable part of the  polynomial and
     discarding inappropriate blocks. Residuals,etc, are calculated by
     evaluating the estimated model as a  TSmodel/ARMA with the data
     (ie. residuals are not the residuals from the regression).

     Note: ar uses a Yule-Walker approach (uses autocorrelations) so
     effectively the  model is for data with means removed. Thus
     subtract.means does not make much difference and re.add.means must
     be TRUE to get back to a model for the  original data.

     The conventon for AR(0)  and sign are changed to ARMA format. Data
     should be of class TSdata. The exog. variable is shifted so
     contemporaneous effects enter. the model for the exog. variable
     (as estimated by ar() is  discarded.

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

     A TSestModel object containing an ARMA TSmodel object. The model
     has no MA portion so it 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' 'estVARXls' 'ar' 'DSE.ar'

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

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

