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: IRF Monte Carlo integrated error bands of cumulative effect
docstr_tok: ["IRF","Monte","Carlo","integrated","error","bands","of","cumulative","effect"]
code: def cum_errband_mc(
      self, orth=False, repl=1000, signif=0.05, seed=None, burn=100
  ):
      """
      IRF Monte Carlo integrated error bands of cumulative effect
      """
      model = self.model
      periods = self.periods
      return model.irf_errband_mc(
          orth=orth,
          repl=repl,
          steps=periods,
          signif=signif,
          seed=seed,
          burn=burn,
          cum=True,
      )
  
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 11 for calling the IRAnalysis obj's underlying member method code with example usage: obj.cum_errband_mc(orth, repl, signif, seed, burn) and returns: model