class_docstr: Results class for the Portmanteau-test for residual autocorrelation.
  
  Parameters
  ----------
  test_statistic : float
      The test's test statistic.
  crit_value : float
      The test's critical value.
  pvalue : float
      The test's p-value.
  df : int
      Degrees of freedom.
  signif : float
      Significance level.
  nlags : int
      Number of lags tested.
class_docstr_tok: ["Results","class","for","the","Portmanteau-test","for","residual","autocorrelation",".","Parameters","--","--","--","--","--","test_statistic",":","float","The","test","'s","test","statistic",".","crit_value",":","float","The","test","'s","critical","value",".","pvalue",":","float","The","test","'s","p-value",".","df",":","int","Degrees","of","freedom",".","signif",":","float","Significance","level",".","nlags",":","int","Number","of","lags","tested","."]
code: def __init__(
      self,
      test_statistic,
      crit_value,
      pvalue,
      df,
      signif,
      nlags,
      adjusted,
  ):
      self.lags = nlags
      self.adjusted = adjusted
      method = "Portmanteau"
      title = f"{method}-test for residual autocorrelation"
      if adjusted:
          title = "Adjusted " + title
      h0 = f"H_0: residual autocorrelation up to lag {nlags} is zero"
      super().__init__(
          test_statistic,
          crit_value,
          pvalue,
          df,
          signif,
          method,
          title,
          h0,
      )
  
file_path: statsmodels/statsmodels/tsa/vector_ar/hypothesis_test_results.py
imports: import numpy
  from statsmodels.iolib.table import SimpleTable
label_desc: Use image node_id 1 to create a new WhitenessTestResults object from inherited base classes: HypothesisTestResults with example: obj = WhitenessTestResults(test_statistic, crit_value, pvalue, df, signif, nlags, adjusted)