class_docstr: class to keep track of Varma polynomial format
  
  
  Examples
  --------
  
  ar23 = np.array([[[ 1. ,  0. ],
                   [ 0. ,  1. ]],
  
                  [[-0.6,  0. ],
                   [ 0.2, -0.6]],
  
                  [[-0.1,  0. ],
                   [ 0.1, -0.1]]])
  
  ma22 = np.array([[[ 1. ,  0. ],
                   [ 0. ,  1. ]],
  
                  [[ 0.4,  0. ],
                   [ 0.2, 0.3]]])
class_docstr_tok: ["class","to","keep","track","of","Varma","polynomial","format","Examples","--","--","--","--","ar23","=","np.array","(","[","[","[","1.",",","0",".","]",",","[","0.",",","1",".","]","]",",","[","[","-0.6",",","0",".","]",",","[","0.2",",","-0.6","]","]",",","[","[","-0.1",",","0",".","]",",","[","0.1",",","-0.1","]","]","]",")","ma22","=","np.array","(","[","[","[","1.",",","0",".","]",",","[","0.",",","1",".","]","]",",","[","[","0.4",",","0",".","]",",","[","0.2",",","0.3","]","]","]",")"]
code: def __init__(self, ar, ma=None):
      self.ar = ar
      self.ma = ma
      nlags, nvarall, nvars = ar.shape
      self.nlags, self.nvarall, self.nvars = nlags, nvarall, nvars
      self.isstructured = not (ar[0, :nvars] == np.eye(nvars)).all()
      if self.ma is None:
          self.ma = np.eye(nvars)[None, ...]
          self.isindependent = True
      else:
          self.isindependent = not (ma[0] == np.eye(nvars)).all()
      self.malags = ar.shape[0]
      self.hasexog = nvarall > nvars
      self.arm1 = -ar[1:]
  
imports: import numpy
  from scipy import signal
  from statsmodels.tsa.tsatools import lagmat
label_desc: Use image node_id 1 to create a new VarmaPoly object  with example: obj = VarmaPoly(ar, ma)