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