class_docstr: Class to hold results from fitting a recursive least squares model.
  
  Parameters
  ----------
  model : RecursiveLS instance
      The fitted model instance
  
  Attributes
  ----------
  specification : dictionary
      Dictionary including all attributes from the recursive least squares
      model instance.
  
  See Also
  --------
  statsmodels.tsa.statespace.kalman_filter.FilterResults
  statsmodels.tsa.statespace.mlemodel.MLEResults
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docstr: Notes
  -----
  Comparing against the cusum6 package for Stata, this does not produce
  exactly the same confidence bands (which are produced in cusum6 by
  lww, uww) because they use a different method for computing the
  critical value; in particular, they use tabled values from
  Table C, pp. 364-365 of "The Econometric Analysis of Time Series"
  Harvey, (1990), and use the value given to 99 observations for any
  larger number of observations. In contrast, we use the approximating
  critical values suggested in Edgerton and Wells (1994) which allows
  computing relatively good approximations for any number of
  observations.
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code: def _cusum_squares_significance_bounds(self, alpha, points=None):
      """
      Notes
      -----
      Comparing against the cusum6 package for Stata, this does not produce
      exactly the same confidence bands (which are produced in cusum6 by
      lww, uww) because they use a different method for computing the
      critical value; in particular, they use tabled values from
      Table C, pp. 364-365 of "The Econometric Analysis of Time Series"
      Harvey, (1990), and use the value given to 99 observations for any
      larger number of observations. In contrast, we use the approximating
      critical values suggested in Edgerton and Wells (1994) which allows
      computing relatively good approximations for any number of
      observations.
      """
      # Get the approximate critical value associated with the significance
      # level
      d = max(self.nobs_diffuse, self.loglikelihood_burn)
      n = 0.5 * (self.nobs - d) - 1
      try:
          ix = [0.1, 0.05, 0.025, 0.01, 0.005].index(alpha / 2)
      except ValueError:
          raise ValueError("Invalid significance level.")
      scalars = _cusum_squares_scalars[:, ix]
      crit = (
          scalars[0] / n**0.5 + scalars[1] / n + scalars[2] / n**1.5
      )
  
      # Get the points for the significance bound lines
      if points is None:
          points = np.array([d, self.nobs])
      line = (points - d) / (self.nobs - d)
  
      return line - crit, line + crit
  
imports: import numpy
  import pandas
  from statsmodels.compat.pandas import Appender
  from statsmodels.tools.data import _is_using_pandas
  from statsmodels.tsa.statespace.mlemodel import MLEModel, MLEResults, MLEResultsWrapper, PredictionResults, PredictionResultsWrapper
  from statsmodels.tsa.statespace.tools import concat
  from statsmodels.tools.tools import Bunch
  from statsmodels.tools.decorators import cache_readonly
  import statsmodels.base.wrapper
label_desc: Use image node_id 20 for calling the RecursiveLSResults obj's underlying member method code with example usage: obj._cusum_squares_significance_bounds(alpha, points) and returns: unknown,  unknown