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]]])
class_docstr_tok: ["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","]","]","]",")"]
docstr: calculate estimated timeseries (yhat) for sample
  
          
docstr_tok: ["calculate","estimated","timeseries","(","yhat",")","for","sample"]
code: def predict(self):
      """calculate estimated timeseries (yhat) for sample"""
  
      if not hasattr(self, "yhat"):
          self.yhat = varfilter(self.y, self.arhat)
      return self.yhat
  
imports: import numpy
  from scipy import signal
  from statsmodels.tsa.tsatools import lagmat
label_desc: Use image node_id 3 for calling the _Var obj's underlying member method code with example usage: obj.predict() and returns: self