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: covariance matrix of estimate
  # not sure it's correct, need to check orientation everywhere
  # looks ok, display needs getting used to
  >>> v.rss[None,None,:]*np.linalg.inv(np.dot(v.xred.T,v.xred))[:,:,None]
  array([[[ 0.37247445,  0.32210609],
          [ 0.1002642 ,  0.08670584]],
  
         [[ 0.1002642 ,  0.08670584],
          [ 0.45903637,  0.39696255]]])
  >>>
  >>> v.rss[0]*np.linalg.inv(np.dot(v.xred.T,v.xred))
  array([[ 0.37247445,  0.1002642 ],
         [ 0.1002642 ,  0.45903637]])
  >>> v.rss[1]*np.linalg.inv(np.dot(v.xred.T,v.xred))
  array([[ 0.32210609,  0.08670584],
         [ 0.08670584,  0.39696255]])
docstr_tok: ["covariance","matrix","of","estimate","#","not","sure","it","'s","correct",",","need","to","check","orientation","everywhere","#","looks","ok",",","display","needs","getting","used","to",">",">",">","v.rss","[","None",",","None",",",":","]","*","np.linalg.inv","(","np.dot","(","v.xred.T",",","v.xred",")",")","[",":",",",":",",None","]","array","(","[","[","[","0.37247445",",","0.32210609","]",",","[","0.1002642",",","0.08670584","]","]",",","[","[","0.1002642",",","0.08670584","]",",","[","0.45903637",",","0.39696255","]","]","]",")",">",">",">",">",">",">","v.rss","[","0","]","*","np.linalg.inv","(","np.dot","(","v.xred.T",",","v.xred",")",")","array","(","[","[","0.37247445",",","0.1002642","]",",","[","0.1002642",",","0.45903637","]","]",")",">",">",">","v.rss","[","1","]","*","np.linalg.inv","(","np.dot","(","v.xred.T",",","v.xred",")",")","array","(","[","[","0.32210609",",","0.08670584","]",",","[","0.08670584",",","0.39696255","]","]",")"]
code: def covmat(self):
      """covariance matrix of estimate
      # not sure it's correct, need to check orientation everywhere
      # looks ok, display needs getting used to
      >>> v.rss[None,None,:]*np.linalg.inv(np.dot(v.xred.T,v.xred))[:,:,None]
      array([[[ 0.37247445,  0.32210609],
              [ 0.1002642 ,  0.08670584]],
  
             [[ 0.1002642 ,  0.08670584],
              [ 0.45903637,  0.39696255]]])
      >>>
      >>> v.rss[0]*np.linalg.inv(np.dot(v.xred.T,v.xred))
      array([[ 0.37247445,  0.1002642 ],
             [ 0.1002642 ,  0.45903637]])
      >>> v.rss[1]*np.linalg.inv(np.dot(v.xred.T,v.xred))
      array([[ 0.32210609,  0.08670584],
             [ 0.08670584,  0.39696255]])
      """
  
      # check if orientation is same as self.arhat
      self.paramcov = (
          self.rss[None, None, :]
          * np.linalg.inv(np.dot(self.xred.T, self.xred))[:, :, None]
      )
  
imports: import numpy
  from scipy import signal
  from statsmodels.tsa.tsatools import lagmat
label_desc: Use image node_id 4 for calling the _Var obj's underlying member method code with example usage: obj.covmat() without return types