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"]
docstr: Compute asymptotic standard errors for impulse response coefficients
  
  Notes
  -----
  Lütkepohl eq 3.7.5
  
  Returns
  -------
docstr_tok: ["Compute","asymptotic","standard","errors","for","impulse","response","coefficients","Notes","--","--","-","L\u00fctkepohl","eq","3.7.5","Returns","--","--","--","-"]
code: def cov(self, orth=False):
      """
      Compute asymptotic standard errors for impulse response coefficients
  
      Notes
      -----
      Lütkepohl eq 3.7.5
  
      Returns
      -------
      """
      if orth:
          return self._orth_cov()
  
      covs = self._empty_covm(self.periods + 1)
      covs[0] = np.zeros((self.neqs**2, self.neqs**2))
      for i in range(1, self.periods + 1):
          Gi = self.G[i - 1]
          covs[i] = Gi @ self.cov_a @ Gi.T
  
      return covs
  
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 2 for calling the IRAnalysis obj's underlying member method code with example usage: obj.cov(orth) and returns: covs, self