class_docstr: Inherits time series methods from TSM class.
  
  **** VECTOR AUTOREGRESSION (VAR) MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      Field to specify the time series data that will be used.
  
  lags : int
      Field to specify how many lag terms the model will have. 
  
  integ : int (default : 0)
      Specifies how many time to difference the dependent variables.
  
  target : str (pd.DataFrame) or int (np.array)
      Specifies which column name or array index to use. By default, first
      column/array will be selected as the dependent variable.
  
  use_ols_covariance : Boolean
      If true use OLS covariance; if false use estimated covariance
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docstr: Creates the structure of the model
  
  Parameters
  ----------
  beta : np.array
      Contains untransformed starting values for latent variables
  
  Returns
  ----------
  mu : np.array
      Contains the predicted values for the time series
  
  Y : np.array
      Contains the length-adjusted time series (accounting for lags)
docstr_tok: ["Creates","the","structure","of","the","model","Parameters","--","--","--","--","--","beta",":","np.array","Contains","untransformed","starting","values","for","latent","variables","Returns","--","--","--","--","--","mu",":","np.array","Contains","the","predicted","values","for","the","time","series","Y",":","np.array","Contains","the","length-adjusted","time","series","(","accounting","for","lags",")"]
code: def _model(self, beta):
      """Creates the structure of the model
  
      Parameters
      ----------
      beta : np.array
          Contains untransformed starting values for latent variables
  
      Returns
      ----------
      mu : np.array
          Contains the predicted values for the time series
  
      Y : np.array
          Contains the length-adjusted time series (accounting for lags)
      """
  
      Y = np.array([reg[self.lags : reg.shape[0]] for reg in self.data])
  
      # Transform latent variables
      beta = np.array(
          [
              self.latent_variables.z_list[k].prior.transform(beta[k])
              for k in range(beta.shape[0])
          ]
      )
  
      params = []
      col_length = 1 + self.ylen * self.lags
      for i in range(0, self.ylen):
          params.append(beta[(col_length * i) : (col_length * (i + 1))])
  
      mu = np.dot(np.array(params), self._create_Z(Y))
      return mu, Y
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  import datetime
  from ..None import families
  from ..None import output
  from ..None import tests
  from ..None import tsm
  from ..None import data_check
  from .var_recursions import create_design_matrix, custom_covariance_matrix, var_likelihood
label_desc: Use image node_id 7 for calling the VAR obj's underlying member method code with example usage: obj._model(beta) and returns: mu,  Y