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: Recursive residuals
  
  Returns
  -------
  resid_recursive : array_like
      An array of length `nobs` holding the recursive
      residuals.
  
  Notes
  -----
  These quantities are defined in, for example, Harvey (1989)
  section 5.4. In fact, there he defines the standardized innovations in
  equation 5.4.1, but in his version they have non-unit variance, whereas
  the standardized forecast errors computed by the Kalman filter here
  assume unit variance. To convert to Harvey's definition, we need to
  multiply by the standard deviation.
  
  Harvey notes that in smaller samples, "although the second moment
  of the :math:`\tilde \sigma_*^{-1} \tilde v_t`'s is unity, the
  variance is not necessarily equal to unity as the mean need not be
  equal to zero", and he defines an alternative version (which are
  not provided here).
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code: def resid_recursive(self):
      r"""
      Recursive residuals
  
      Returns
      -------
      resid_recursive : array_like
          An array of length `nobs` holding the recursive
          residuals.
  
      Notes
      -----
      These quantities are defined in, for example, Harvey (1989)
      section 5.4. In fact, there he defines the standardized innovations in
      equation 5.4.1, but in his version they have non-unit variance, whereas
      the standardized forecast errors computed by the Kalman filter here
      assume unit variance. To convert to Harvey's definition, we need to
      multiply by the standard deviation.
  
      Harvey notes that in smaller samples, "although the second moment
      of the :math:`\tilde \sigma_*^{-1} \tilde v_t`'s is unity, the
      variance is not necessarily equal to unity as the mean need not be
      equal to zero", and he defines an alternative version (which are
      not provided here).
      """
      return (
          self.filter_results.standardized_forecasts_error[0]
          * self.scale**0.5
      )
  
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 3 for calling the RecursiveLSResults obj's underlying member method code with example usage: obj.resid_recursive() and returns: unknown