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
class_docstr_tok: ["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"]
docstr: Estimates of regression coefficients, recursively estimated
  
  Returns
  -------
  out: Bunch
      Has the following attributes:
  
      - `filtered`: a time series array with the filtered estimate of
                    the component
      - `filtered_cov`: a time series array with the filtered estimate of
                    the variance/covariance of the component
      - `smoothed`: a time series array with the smoothed estimate of
                    the component
      - `smoothed_cov`: a time series array with the smoothed estimate of
                    the variance/covariance of the component
      - `offset`: an integer giving the offset in the state vector where
                  this component begins
docstr_tok: ["Estimates","of","regression","coefficients",",","recursively","estimated","Returns","--","--","--","-","out",":","Bunch","Has","the","following","attributes",":","-","`","filtered","`",":","a","time","series","array","with","the","filtered","estimate","of","the","component","-","`","filtered_cov","`",":","a","time","series","array","with","the","filtered","estimate","of","the","variance\/covariance","of","the","component","-","`","smoothed","`",":","a","time","series","array","with","the","smoothed","estimate","of","the","component","-","`","smoothed_cov","`",":","a","time","series","array","with","the","smoothed","estimate","of","the","variance\/covariance","of","the","component","-","`","offset","`",":","an","integer","giving","the","offset","in","the","state","vector","where","this","component","begins"]
code: def recursive_coefficients(self):
      """
      Estimates of regression coefficients, recursively estimated
  
      Returns
      -------
      out: Bunch
          Has the following attributes:
  
          - `filtered`: a time series array with the filtered estimate of
                        the component
          - `filtered_cov`: a time series array with the filtered estimate of
                        the variance/covariance of the component
          - `smoothed`: a time series array with the smoothed estimate of
                        the component
          - `smoothed_cov`: a time series array with the smoothed estimate of
                        the variance/covariance of the component
          - `offset`: an integer giving the offset in the state vector where
                      this component begins
      """
      out = None
      spec = self.specification
      start = offset = 0
      end = offset + spec.k_exog
      out = Bunch(
          filtered=self.filtered_state[start:end],
          filtered_cov=self.filtered_state_cov[start:end, start:end],
          smoothed=None,
          smoothed_cov=None,
          offset=offset,
      )
      if self.smoothed_state is not None:
          out.smoothed = self.smoothed_state[start:end]
      if self.smoothed_state_cov is not None:
          out.smoothed_cov = self.smoothed_state_cov[
              start:end, start:end
          ]
      return out
  
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 2 for calling the RecursiveLSResults obj's underlying member method code with example usage: obj.recursive_coefficients() and returns: out