class_docstr: obsolete VAR class, use tsa.VAR instead, for internal use only
  
  
  Examples
  --------
  
  >>> v = Var(ar2s)
  >>> v.fit(1)
  >>> v.arhat
  array([[[ 1.        ,  0.        ],
          [ 0.        ,  1.        ]],
  
         [[-0.77784898,  0.01726193],
          [ 0.10733009, -0.78665335]]])
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docstr: calculates forcast for horiz number of periods at end of sample
  
  Parameters
  ----------
  horiz : int (optional, default=1)
      forecast horizon
  u : array (horiz, nvars)
      error term for forecast periods. If None, then u is zero.
  
  Returns
  -------
  yforecast : array (nobs+horiz, nvars)
      this includes the sample and the forecasts
docstr_tok: ["calculates","forcast","for","horiz","number","of","periods","at","end","of","sample","Parameters","--","--","--","--","--","horiz",":","int","(","optional",",","default=1",")","forecast","horizon","u",":","array","(","horiz",",","nvars",")","error","term","for","forecast","periods",".","If","None",",","then","u","is","zero",".","Returns","--","--","--","-","yforecast",":","array","(","nobs+horiz",",","nvars",")","this","includes","the","sample","and","the","forecasts"]
code: def forecast(self, horiz=1, u=None):
      """calculates forcast for horiz number of periods at end of sample
  
      Parameters
      ----------
      horiz : int (optional, default=1)
          forecast horizon
      u : array (horiz, nvars)
          error term for forecast periods. If None, then u is zero.
  
      Returns
      -------
      yforecast : array (nobs+horiz, nvars)
          this includes the sample and the forecasts
      """
      if u is None:
          u = np.zeros((horiz, self.nvars))
      return vargenerate(self.arhat, u, initvalues=self.y)
  
imports: import numpy
  from scipy import signal
  from statsmodels.tsa.tsatools import lagmat
label_desc: Use image node_id 5 for calling the _Var obj's underlying member method code with example usage: obj.forecast(horiz, u) and returns: vargenerate