class_docstr: Class to hold results from fitting an unobserved components model.
  
  Parameters
  ----------
  model : UnobservedComponents instance
      The fitted model instance
  
  Attributes
  ----------
  specification : dictionary
      Dictionary including all attributes from the unobserved components
      model instance.
  
  See Also
  --------
  statsmodels.tsa.statespace.kalman_filter.FilterResults
  statsmodels.tsa.statespace.mlemodel.MLEResults
class_docstr_tok: ["Class","to","hold","results","from","fitting","an","unobserved","components","model",".","Parameters","--","--","--","--","--","model",":","UnobservedComponents","instance","The","fitted","model","instance","Attributes","--","--","--","--","--","specification",":","dictionary","Dictionary","including","all","attributes","from","the","unobserved","components","model","instance",".","See","Also","--","--","--","--","statsmodels.tsa.statespace.kalman_filter.FilterResults","statsmodels.tsa.statespace.mlemodel.MLEResults"]
docstr: Estimates of unobserved regression coefficients
  
  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","unobserved","regression","coefficients","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 regression_coefficients(self):
      """
      Estimates of unobserved regression coefficients
  
      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
      """
      # If present, state-vector regression coefficients always are last
      # (i.e. they follow level/trend, seasonal, freq seasonal, cyclical, and
      # autoregressive states). There is one state associated with each
      # regressor, and all are returned here.
      out = None
      spec = self.specification
      if spec.regression:
          if spec.mle_regression:
              import warnings
  
              warnings.warn(
                  "Regression coefficients estimated via maximum"
                  " likelihood. Estimated coefficients are"
                  " available in the parameters list, not as part"
                  " of the state vector.",
                  OutputWarning,
              )
          else:
              offset = int(
                  spec.trend
                  + spec.level
                  + self._k_states_by_type["seasonal"]
                  + self._k_states_by_type["freq_seasonal"]
                  + self._k_states_by_type["cycle"]
                  + spec.ar_order
              )
              start = offset
              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: from warnings import warn
  import numpy
  from statsmodels.compat.pandas import Appender
  from statsmodels.tools.tools import Bunch
  from statsmodels.tools.sm_exceptions import OutputWarning, SpecificationWarning
  import statsmodels.base.wrapper
  from statsmodels.tsa.filters.hp_filter import hpfilter
  from statsmodels.tsa.tsatools import lagmat
  from .mlemodel import MLEModel, MLEResults, MLEResultsWrapper
  from .initialization import Initialization
  from .tools import companion_matrix, constrain_stationary_univariate, unconstrain_stationary_univariate, prepare_exog
label_desc: Use image node_id 8 for calling the UnobservedComponentsResults obj's underlying member method code with example usage: obj.regression_coefficients() and returns: out