class_docstr: Impulse response analysis class. Computes impulse responses, asymptotic
  standard errors, and produces relevant plots
  
  Parameters
  ----------
  model : VAR instance
  
  Notes
  -----
  Using Lütkepohl (2005) notation
class_docstr_tok: ["Impulse","response","analysis","class",".","Computes","impulse","responses",",","asymptotic","standard","errors",",","and","produces","relevant","plots","Parameters","--","--","--","--","--","model",":","VAR","instance","Notes","--","--","-","Using","L\u00fctkepohl","(","2005",")","notation"]
code: def H(self):
      k = self.neqs
      Lk = tsa.elimination_matrix(k)
      Kkk = tsa.commutation_matrix(k, k)
      Ik = np.eye(k)
  
      # B = Lk @ (np.eye(k**2) + commutation_matrix(k, k)) @ \
      #     np.kron(self.P, np.eye(k)) @ Lk.T
      # return Lk.T @ L.inv(B)
  
      B = Lk @ (np.kron(Ik, self.P) @ Kkk + np.kron(self.P, Ik)) @ Lk.T
  
      return np.dot(Lk.T, L.inv(B))
  
imports: import numpy
  import numpy.linalg
  import scipy.linalg
  from statsmodels.tools.decorators import cache_readonly
  import statsmodels.tsa.tsatools
  import statsmodels.tsa.vector_ar.plotting
  import statsmodels.tsa.vector_ar.util
label_desc: Use image node_id 17 for calling the IRAnalysis obj's underlying member method code with example usage: obj.H() and returns: np